Persistent network model diagnostics - unbalanced statistics
This file shows diagnostics for persistent network models fit using unbalanced racial/ethnic mixing matrices and degree terms as reported by egos. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.
Load packages and model fits
rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")## Loading required package: lattice## Loading required package: RColorBrewerlibrary("knitr")
library("kableExtra")
library("here")## here() starts at /homes/dpwhite/R/GitHub Repos/WHAMPload(file = here("Model fits and simulations/Fit tests and debugging/est/fit.p.buildup.unbal.rda"))Model terms and control settings
| Terms | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
|---|---|---|---|---|---|---|---|---|
| edges | 2018 | 2018.0 | 2018.0 | 2018.0 | 2018.0 | 2018.0 | 2018.0 | 2018.0 |
| nodefactor.deg.main.1 | NA | NA | NA | 1684.0 | 1684.0 | 1684.0 | 1684.0 | 1684.0 |
| nodefactor.race..wa.B | NA | 251.2 | 251.2 | 251.2 | 251.2 | 251.2 | 251.2 | 251.2 |
| nodefactor.race..wa.H | NA | 388.9 | 388.9 | 388.9 | 388.9 | 388.9 | 388.9 | 388.9 |
| nodefactor.region.EW | NA | NA | NA | NA | 367.7 | 367.7 | 367.7 | 367.7 |
| nodefactor.region.OW | NA | NA | NA | NA | 1182.5 | 1182.5 | 1182.5 | 1182.5 |
| concurrent | NA | NA | NA | NA | NA | NA | 1385.0 | 1385.0 |
| nodematch.race..wa.B | NA | NA | 8.5 | 8.5 | 8.5 | 8.5 | 8.5 | 8.5 |
| nodematch.race..wa.H | NA | NA | 51.2 | 51.2 | 51.2 | 51.2 | 51.2 | 51.2 |
| nodematch.race..wa.O | NA | NA | 1246.8 | 1246.8 | 1246.8 | 1246.8 | 1246.8 | 1246.8 |
| nodematch.region | NA | NA | NA | NA | NA | NA | NA | 1614.4 |
| absdiff.sqrt.age | NA | NA | NA | NA | NA | 1665.3 | 1665.3 | 1665.3 |
| degrange | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| nodematch.role.class.I | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
| nodematch.role.class.R | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
The control settings for these models are:
set.control.ergm = ccontrol.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
init.method = "zeros",
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
Model 1
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## -0.2186 40.1536 0.2318 0.2312
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## -79 -27 0 27 79
##
##
## Sample statistics cross-correlations:
## edges
## edges 1
##
## Sample statistics auto-correlation:
## Chain 1
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.013487554
## Lag 2e+05 -0.035782824
## Lag 3e+05 -0.020532232
## Lag 4e+05 0.007817567
## Lag 5e+05 0.003661391
## Chain 2
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.001671115
## Lag 2e+05 -0.022463497
## Lag 3e+05 -0.012324236
## Lag 4e+05 -0.010816892
## Lag 5e+05 -0.008168326
## Chain 3
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.008966853
## Lag 2e+05 0.005485714
## Lag 3e+05 -0.045421713
## Lag 4e+05 -0.006897222
## Lag 5e+05 0.031279296
## Chain 4
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.005000925
## Lag 2e+05 0.008226031
## Lag 3e+05 0.003451454
## Lag 4e+05 0.011273038
## Lag 5e+05 -0.028053163
## Chain 5
## edges
## Lag 0 1.0000000000
## Lag 1e+05 0.0020077954
## Lag 2e+05 -0.0103842854
## Lag 3e+05 -0.0003697404
## Lag 4e+05 0.0233826165
## Lag 5e+05 0.0177610448
## Chain 6
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.023611076
## Lag 2e+05 0.002694384
## Lag 3e+05 0.005469468
## Lag 4e+05 0.002845718
## Lag 5e+05 -0.001076281
## Chain 7
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.006678144
## Lag 2e+05 0.015121069
## Lag 3e+05 -0.004985953
## Lag 4e+05 -0.003576797
## Lag 5e+05 -0.017363692
## Chain 8
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.011481261
## Lag 2e+05 0.017588090
## Lag 3e+05 0.004523673
## Lag 4e+05 -0.052315339
## Lag 5e+05 -0.009571548
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.3126
##
## Individual P-values (lower = worse):
## edges
## 0.7546014
## Joint P-value (lower = worse): 0.7487839 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -1.36
##
## Individual P-values (lower = worse):
## edges
## 0.1737631
## Joint P-value (lower = worse): 0.188632 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 1.114
##
## Individual P-values (lower = worse):
## edges
## 0.2653338
## Joint P-value (lower = worse): 0.262925 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.7309
##
## Individual P-values (lower = worse):
## edges
## 0.4648672
## Joint P-value (lower = worse): 0.4673233 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.7366
##
## Individual P-values (lower = worse):
## edges
## 0.461344
## Joint P-value (lower = worse): 0.4586529 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.6506
##
## Individual P-values (lower = worse):
## edges
## 0.5152947
## Joint P-value (lower = worse): 0.5125573 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -1.152
##
## Individual P-values (lower = worse):
## edges
## 0.249164
## Joint P-value (lower = worse): 0.2434573 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.06897
##
## Individual P-values (lower = worse):
## edges
## 0.9450113
## Joint P-value (lower = worse): 0.9432202 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 2
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.3733 40.35 0.23296 0.23294
## nodefactor.race..wa.B 0.5469 15.32 0.08846 0.09042
## nodefactor.race..wa.H -0.0133 19.59 0.11311 0.11268
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -78.00 -27.00 0.0000 28.00 79.00
## nodefactor.race..wa.B -29.16 -10.16 0.8352 10.84 30.84
## nodefactor.race..wa.H -37.91 -12.91 0.0920 13.09 39.09
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.32050628
## nodefactor.race..wa.B 0.3205063 1.00000000
## nodefactor.race..wa.H 0.4054482 0.04918763
## nodefactor.race..wa.H
## edges 0.40544823
## nodefactor.race..wa.B 0.04918763
## nodefactor.race..wa.H 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.006554221 -0.006717729 -0.009683622
## Lag 2e+05 0.011957280 -0.029559718 0.010268905
## Lag 3e+05 0.004139475 -0.019093137 0.001464487
## Lag 4e+05 -0.004938479 0.003280830 -0.010539335
## Lag 5e+05 -0.024362461 0.001665306 -0.013782424
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.01194897 0.026728225 0.011213785
## Lag 2e+05 -0.01997238 -0.035150735 0.009879885
## Lag 3e+05 -0.03554968 0.003859359 -0.007344619
## Lag 4e+05 -0.02084855 0.022976757 -0.004207365
## Lag 5e+05 0.02966213 -0.014637060 -0.003341877
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.032117295 -0.016584543 0.0230555989
## Lag 2e+05 0.007444965 0.017934058 -0.0020167452
## Lag 3e+05 0.008080309 -0.016135064 -0.0171629171
## Lag 4e+05 -0.013252445 0.004144110 0.0011410827
## Lag 5e+05 -0.030043649 0.001505045 0.0009293615
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.016147357 0.019427602 -0.003433325
## Lag 2e+05 0.003957918 -0.006910936 -0.002167345
## Lag 3e+05 0.021268152 0.024385735 -0.010024469
## Lag 4e+05 -0.016129658 -0.030616004 0.012822134
## Lag 5e+05 0.010888433 -0.009133953 -0.004842446
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.008645006 0.011780900 0.004060910
## Lag 2e+05 0.004953322 0.005421545 0.002295745
## Lag 3e+05 -0.020208704 0.016646870 -0.010157812
## Lag 4e+05 -0.001419087 0.002286020 -0.002614842
## Lag 5e+05 -0.027391459 -0.004234575 -0.022352739
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.00000000 1.0000000000 1.000000000
## Lag 1e+05 0.01016942 0.0544214362 -0.007005882
## Lag 2e+05 0.02545053 -0.0005276596 -0.011545210
## Lag 3e+05 -0.01960948 0.0262297090 0.008377690
## Lag 4e+05 -0.00640942 0.0083933890 -0.008986206
## Lag 5e+05 -0.01410669 0.0069180447 0.018535807
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.004571201 0.014024757 -0.0184098938
## Lag 2e+05 0.014593576 -0.025626125 0.0049212093
## Lag 3e+05 -0.021765919 -0.009416186 -0.0006888469
## Lag 4e+05 -0.026793779 0.009643306 0.0124393078
## Lag 5e+05 -0.024177115 0.031002345 0.0131789891
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.018270499 0.0113923592 -0.0308584005
## Lag 2e+05 -0.016149433 0.0030091822 -0.0016766501
## Lag 3e+05 -0.024231745 0.0005475025 -0.0282665482
## Lag 4e+05 -0.016740363 0.0003332694 -0.0093512908
## Lag 5e+05 -0.008473009 0.0199931748 0.0005746285
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.649138 1.340152 -0.004618
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.09911944 0.18019584 0.99631499
## Joint P-value (lower = worse): 0.2780115 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.3241 0.2388 0.3444
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7458688 0.8112580 0.7305266
## Joint P-value (lower = worse): 0.9055359 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.202 0.483 1.137
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2292330 0.6291053 0.2557139
## Joint P-value (lower = worse): 0.5386789 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.9256 -0.5185 -0.5661
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3546760 0.6041435 0.5713258
## Joint P-value (lower = worse): 0.8017229 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.0189 0.3985 -0.3357
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3082600 0.6902818 0.7371271
## Joint P-value (lower = worse): 0.6614073 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.03285 -0.67817 -0.26158
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9737936 0.4976652 0.7936429
## Joint P-value (lower = worse): 0.8904737 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.79895 1.14838 -0.05557
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4243211 0.2508114 0.9556824
## Joint P-value (lower = worse): 0.4523851 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.62318 0.83140 -0.04352
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.5331635 0.4057493 0.9652908
## Joint P-value (lower = worse): 0.8193655 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 3
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -35.215 40.247 0.23237 0.23592
## nodefactor.race..wa.B 36.843 15.748 0.09092 0.09087
## nodefactor.race..wa.H 36.621 20.095 0.11602 0.11078
## nodematch.race..wa.B -8.477 0.000 0.00000 0.00000
## nodematch.race..wa.H -36.412 3.798 0.02193 0.02198
## nodematch.race..wa.O 37.193 33.131 0.19128 0.19331
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -114.000 -62.000 -35.000 -8.000 44.000
## nodefactor.race..wa.B 6.835 25.835 36.835 47.835 67.835
## nodefactor.race..wa.H -2.908 23.092 36.092 50.092 76.092
## nodematch.race..wa.B -8.477 -8.477 -8.477 -8.477 -8.477
## nodematch.race..wa.H -43.200 -39.200 -36.200 -34.200 -28.200
## nodematch.race..wa.O -27.844 15.156 37.156 59.156 102.156
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges nodefactor.race..wa.B
## edges 1.00000000 0.352221542
## nodefactor.race..wa.B 0.35222154 1.000000000
## nodefactor.race..wa.H 0.44005669 -0.006100363
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.08924169 -0.001999436
## nodematch.race..wa.O 0.79069122 -0.043970280
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 0.440056693 NA
## nodefactor.race..wa.B -0.006100363 NA
## nodefactor.race..wa.H 1.000000000 NA
## nodematch.race..wa.B NA 1
## nodematch.race..wa.H 0.352339220 NA
## nodematch.race..wa.O -0.028680803 NA
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.089241688 0.79069122
## nodefactor.race..wa.B -0.001999436 -0.04397028
## nodefactor.race..wa.H 0.352339220 -0.02868080
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 1.000000000 0.01027457
## nodematch.race..wa.O 0.010274566 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0025395876 -0.007050145 -0.022349892
## Lag 2e+05 0.0002144022 0.017917582 -0.015166343
## Lag 3e+05 -0.0141075319 -0.041900065 -0.009515247
## Lag 4e+05 0.0021146721 0.006754025 -0.020528122
## Lag 5e+05 0.0062868976 0.012217880 0.012964218
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.016832724 -0.002042829
## Lag 2e+05 NaN -0.012535316 0.004680582
## Lag 3e+05 NaN -0.015206862 -0.013417354
## Lag 4e+05 NaN 0.006336900 0.011134573
## Lag 5e+05 NaN -0.003048031 0.005799247
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000e+00 1.000000000 1.000000000
## Lag 1e+05 -4.779389e-03 -0.012476301 -0.029186951
## Lag 2e+05 -1.486821e-02 -0.005992004 -0.014901114
## Lag 3e+05 3.488001e-02 -0.013594430 -0.014482120
## Lag 4e+05 -1.498385e-02 0.010714475 -0.002572354
## Lag 5e+05 6.006984e-05 -0.020866890 0.002347319
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.022882124 0.005282790
## Lag 2e+05 NaN 0.004187335 -0.026090402
## Lag 3e+05 NaN 0.025926740 0.039893801
## Lag 4e+05 NaN 0.011832242 -0.004409353
## Lag 5e+05 NaN -0.001448160 -0.006174433
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.0194752793 -0.013390370 0.0220747724
## Lag 2e+05 0.0118727086 0.003035374 0.0008845601
## Lag 3e+05 -0.0016427349 0.021751905 -0.0037049907
## Lag 4e+05 -0.0005382807 -0.022894406 0.0270578585
## Lag 5e+05 0.0039570083 -0.005416584 0.0306995479
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.017669199 -0.028603654
## Lag 2e+05 NaN -0.004605939 0.016475963
## Lag 3e+05 NaN 0.046496285 -0.010612591
## Lag 4e+05 NaN -0.006054243 0.000485405
## Lag 5e+05 NaN 0.010791942 -0.013620821
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.003167782 0.024710306 -0.032348920
## Lag 2e+05 0.010893139 0.007247245 -0.007991944
## Lag 3e+05 -0.001897216 -0.007249646 -0.030883762
## Lag 4e+05 -0.026879752 -0.006030720 0.007910120
## Lag 5e+05 0.006125311 -0.022027747 0.003457858
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.043936384 0.004604176
## Lag 2e+05 NaN 0.005954585 -0.003465823
## Lag 3e+05 NaN 0.012116516 0.010029710
## Lag 4e+05 NaN 0.020583793 -0.018629889
## Lag 5e+05 NaN -0.002316752 -0.006001492
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.024531164 0.002215175 -0.026526431
## Lag 2e+05 0.015942909 0.021366319 0.014425610
## Lag 3e+05 -0.025675818 0.001476705 0.018609235
## Lag 4e+05 -0.008322712 0.011998448 0.005553276
## Lag 5e+05 0.004232813 0.031509877 0.013265472
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.027817322 -0.007594972
## Lag 2e+05 NaN -0.015737697 0.010442853
## Lag 3e+05 NaN 0.007922660 -0.012306620
## Lag 4e+05 NaN 0.004111782 0.009738144
## Lag 5e+05 NaN 0.019929459 -0.003090154
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.016109397 0.006486776 -0.002069206
## Lag 2e+05 0.022594584 0.017598106 -0.002579903
## Lag 3e+05 -0.014343982 -0.010105222 0.005826830
## Lag 4e+05 0.007106531 0.004821466 -0.016874441
## Lag 5e+05 -0.016346025 -0.007857468 -0.015946328
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000e+00 1.00000000
## Lag 1e+05 NaN 3.685830e-03 0.02369092
## Lag 2e+05 NaN 3.340193e-03 0.02931499
## Lag 3e+05 NaN 1.111162e-02 -0.01851355
## Lag 4e+05 NaN -1.972230e-02 -0.01131314
## Lag 5e+05 NaN -8.716786e-05 -0.03142971
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.010846368 0.0086911478 -0.0145840872
## Lag 2e+05 -0.011821852 -0.0204958427 -0.0140439193
## Lag 3e+05 -0.001132228 -0.0116615284 -0.0003579546
## Lag 4e+05 0.019854873 -0.0354166418 0.0019165291
## Lag 5e+05 -0.022603939 0.0005646085 0.0210214525
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.024081035 -0.028997402
## Lag 2e+05 NaN -0.008079222 0.002907015
## Lag 3e+05 NaN 0.006595599 0.028475684
## Lag 4e+05 NaN -0.031226018 0.007305972
## Lag 5e+05 NaN -0.009933457 -0.011224779
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.006717060 -0.009389680 -0.029230252
## Lag 2e+05 0.013051826 -0.004549981 0.007857429
## Lag 3e+05 -0.007143657 0.016169896 0.011255313
## Lag 4e+05 -0.014246527 -0.012711134 0.011844742
## Lag 5e+05 0.007510439 0.006241833 0.011368162
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.009371426 0.011945128
## Lag 2e+05 NaN -0.012900385 -0.020747355
## Lag 3e+05 NaN -0.007173687 -0.019166803
## Lag 4e+05 NaN 0.024673026 -0.008660007
## Lag 5e+05 NaN -0.017606355 0.006663871
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -2.0167 -1.7950 0.5743
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.6437 -1.7737
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.04373203 0.07265336 0.56579209
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.51977201 0.07611146
## Joint P-value (lower = worse): 0.1515322 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.08428 -1.70156 -0.25364
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 1.26489 0.86057
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.93283063 0.08883818 0.79977014
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.20590910 0.38947232
## Joint P-value (lower = worse): 0.4999851 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.2064 0.7763 -0.5521
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.8283 -1.3337
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2276747 0.4375972 0.5808545
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.4075050 0.1823100
## Joint P-value (lower = worse): 0.5696944 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.08162 1.79350 0.97163
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -0.67882 -1.40428
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.93495210 0.07289275 0.33123643
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.49725087 0.16023639
## Joint P-value (lower = worse): 0.221311 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.001244 1.107957 1.316137
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.396876 -1.349703
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9990075 0.2678804 0.1881280
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.6914592 0.1771113
## Joint P-value (lower = worse): 0.4748407 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.8935 -0.4100 2.1198
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.9054 1.1768
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.05829656 0.68180665 0.03402018
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.36524729 0.23926710
## Joint P-value (lower = worse): 0.2899855 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.5554 0.1886 -1.7236
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 1.6638 0.4090
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.57860000 0.85042651 0.08478907
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.09615893 0.68257155
## Joint P-value (lower = worse): 0.1043284 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.5244 -0.1275 2.0191
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -0.4178 -0.2432
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.60003410 0.89855080 0.04347235
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.67606519 0.80782372
## Joint P-value (lower = worse): 0.7020861 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 4
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -35.1802 40.257 0.23243 0.23264
## nodefactor.deg.main.1 0.5313 45.530 0.26287 0.26195
## nodefactor.race..wa.B 38.5535 15.896 0.09178 0.09031
## nodefactor.race..wa.H 35.8103 20.048 0.11574 0.11512
## nodematch.race..wa.B -8.4768 0.000 0.00000 0.00000
## nodematch.race..wa.H -36.2771 3.813 0.02201 0.02157
## nodematch.race..wa.O 36.4615 33.179 0.19156 0.19092
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -114.000 -62.000 -36.000 -8.000 44.000
## nodefactor.deg.main.1 -89.000 -30.000 1.000 31.000 89.000
## nodefactor.race..wa.B 7.835 27.835 38.835 48.835 69.835
## nodefactor.race..wa.H -2.908 22.092 36.092 49.092 76.092
## nodematch.race..wa.B -8.477 -8.477 -8.477 -8.477 -8.477
## nodematch.race..wa.H -43.200 -39.200 -36.200 -34.200 -28.200
## nodematch.race..wa.O -27.844 14.156 36.156 59.156 102.156
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges nodefactor.deg.main.1
## edges 1.00000000 0.76203367
## nodefactor.deg.main.1 0.76203367 1.00000000
## nodefactor.race..wa.B 0.36268533 0.24190973
## nodefactor.race..wa.H 0.42261409 0.34448074
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.07074788 0.06046846
## nodematch.race..wa.O 0.79234909 0.60750886
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.36268533 0.42261409
## nodefactor.deg.main.1 0.24190973 0.34448074
## nodefactor.race..wa.B 1.00000000 -0.02041070
## nodefactor.race..wa.H -0.02041070 1.00000000
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H -0.01097115 0.34431529
## nodematch.race..wa.O -0.02797774 -0.04210483
## nodematch.race..wa.B nodematch.race..wa.H
## edges NA 0.07074788
## nodefactor.deg.main.1 NA 0.06046846
## nodefactor.race..wa.B NA -0.01097115
## nodefactor.race..wa.H NA 0.34431529
## nodematch.race..wa.B 1 NA
## nodematch.race..wa.H NA 1.00000000
## nodematch.race..wa.O NA -0.00203172
## nodematch.race..wa.O
## edges 0.79234909
## nodefactor.deg.main.1 0.60750886
## nodefactor.race..wa.B -0.02797774
## nodefactor.race..wa.H -0.04210483
## nodematch.race..wa.B NA
## nodematch.race..wa.H -0.00203172
## nodematch.race..wa.O 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.001942744 -0.001140796 -0.011393414
## Lag 2e+05 -0.018540770 -0.010563570 -0.026676522
## Lag 3e+05 -0.001132814 0.004415958 0.021612902
## Lag 4e+05 0.006140194 -0.026929134 0.017553501
## Lag 5e+05 -0.007257402 -0.001448532 0.002410582
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 -0.002987320 NaN 0.006579022
## Lag 2e+05 -0.001870620 NaN 0.016174841
## Lag 3e+05 -0.009269763 NaN -0.019716615
## Lag 4e+05 0.006419384 NaN 0.012717694
## Lag 5e+05 -0.009884298 NaN -0.020071700
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0043658782
## Lag 2e+05 -0.0173723391
## Lag 3e+05 0.0059636003
## Lag 4e+05 0.0003111173
## Lag 5e+05 -0.0138527797
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.018240384 -0.010731653 -0.017962250
## Lag 2e+05 0.001174571 0.002318661 0.013715761
## Lag 3e+05 -0.019734937 0.006713202 0.002375300
## Lag 4e+05 0.030408187 0.011819220 0.002308762
## Lag 5e+05 -0.011850388 -0.001770853 -0.019002206
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 NaN 1.000000000
## Lag 1e+05 -0.02469961 NaN -0.011580188
## Lag 2e+05 -0.01381426 NaN -0.013826362
## Lag 3e+05 -0.04011451 NaN 0.008392337
## Lag 4e+05 0.01848514 NaN -0.026260148
## Lag 5e+05 0.01357708 NaN -0.000684475
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.015211484
## Lag 2e+05 -0.013110202
## Lag 3e+05 -0.005779592
## Lag 4e+05 0.008956427
## Lag 5e+05 0.004704919
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.004461168 -0.005433140 0.0082977157
## Lag 2e+05 0.004522178 -0.011630567 0.0002460714
## Lag 3e+05 0.019502342 0.009312217 0.0009693014
## Lag 4e+05 0.013516937 0.042228828 -0.0094089762
## Lag 5e+05 -0.029633441 -0.049651177 -0.0270108561
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000e+00 NaN 1.000000000
## Lag 1e+05 -7.509683e-05 NaN 0.005441897
## Lag 2e+05 -1.306972e-02 NaN -0.007885470
## Lag 3e+05 4.213026e-02 NaN -0.001924692
## Lag 4e+05 -2.425348e-02 NaN 0.003809971
## Lag 5e+05 4.893917e-03 NaN 0.013341748
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.023693833
## Lag 2e+05 0.017189155
## Lag 3e+05 0.006437583
## Lag 4e+05 0.031118125
## Lag 5e+05 -0.008659570
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.015917421 -0.013822103 -0.020135401
## Lag 2e+05 -0.017976882 -0.014398624 -0.013902637
## Lag 3e+05 -0.003268163 0.001966200 -0.008473962
## Lag 4e+05 -0.010668739 -0.009734738 -0.005538536
## Lag 5e+05 -0.001632559 -0.001151857 0.026177361
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.0000000000
## Lag 1e+05 -0.023727363 NaN -0.0512159131
## Lag 2e+05 -0.002460876 NaN 0.0128604321
## Lag 3e+05 0.020946351 NaN -0.0006981956
## Lag 4e+05 -0.019228003 NaN -0.0010939300
## Lag 5e+05 -0.021952908 NaN -0.0166150379
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.004657759
## Lag 2e+05 -0.004787134
## Lag 3e+05 0.007837227
## Lag 4e+05 -0.013544327
## Lag 5e+05 -0.006901445
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.024946867 -0.010931328 0.008848543
## Lag 2e+05 0.012760437 0.005431661 0.009138981
## Lag 3e+05 0.011060827 0.023530683 0.018187254
## Lag 4e+05 0.014898162 -0.008795485 0.002548699
## Lag 5e+05 -0.007714017 -0.007163429 -0.010384990
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000e+00
## Lag 1e+05 0.018876804 NaN -9.344338e-05
## Lag 2e+05 0.001652251 NaN 9.308547e-03
## Lag 3e+05 0.008876304 NaN -1.610398e-02
## Lag 4e+05 0.005615181 NaN 9.984577e-03
## Lag 5e+05 -0.004204053 NaN -1.302465e-02
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.012835382
## Lag 2e+05 -0.001167879
## Lag 3e+05 -0.005758339
## Lag 4e+05 0.004927961
## Lag 5e+05 0.008854732
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.001513901 0.005874539 -0.006091326
## Lag 2e+05 0.033282908 0.021125859 -0.011534700
## Lag 3e+05 0.005796159 -0.005625543 0.006531761
## Lag 4e+05 -0.005969890 -0.010605392 0.018051583
## Lag 5e+05 0.007489625 0.009321633 0.002724554
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 -0.025788213 NaN 0.011286046
## Lag 2e+05 0.017441786 NaN 0.005402040
## Lag 3e+05 -0.026582428 NaN -0.023272345
## Lag 4e+05 -0.014636136 NaN 0.028877717
## Lag 5e+05 -0.005894719 NaN 0.003447072
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.019623596
## Lag 2e+05 0.021227037
## Lag 3e+05 0.016545151
## Lag 4e+05 -0.018815185
## Lag 5e+05 0.003545777
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.01434024 0.004626503 -0.017021639
## Lag 2e+05 -0.01476897 -0.027135200 0.028141375
## Lag 3e+05 -0.03080730 -0.031366294 -0.025696652
## Lag 4e+05 0.01072404 0.020682187 -0.001998814
## Lag 5e+05 0.00959724 0.019394000 0.016012455
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 0.031523119 NaN -0.006203119
## Lag 2e+05 -0.001869240 NaN -0.017257842
## Lag 3e+05 -0.016275732 NaN 0.012030352
## Lag 4e+05 0.009931136 NaN -0.004597282
## Lag 5e+05 -0.001119268 NaN -0.019603059
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.019497629
## Lag 2e+05 -0.027067109
## Lag 3e+05 0.002886415
## Lag 4e+05 -0.007819070
## Lag 5e+05 0.008209035
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.012112947 0.03390803 0.021788021
## Lag 2e+05 -0.013687213 -0.01605194 -0.044347256
## Lag 3e+05 0.025312496 0.02074258 0.007723428
## Lag 4e+05 0.004385529 0.01633225 0.032585810
## Lag 5e+05 -0.009825275 -0.01775431 0.010566571
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000e+00
## Lag 1e+05 -0.008674521 NaN -7.898772e-03
## Lag 2e+05 0.001154270 NaN 1.344878e-02
## Lag 3e+05 -0.004966047 NaN -3.771578e-04
## Lag 4e+05 0.047970901 NaN 5.632767e-05
## Lag 5e+05 -0.007985781 NaN -1.982353e-02
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.012142338
## Lag 2e+05 -0.007916641
## Lag 3e+05 0.026838489
## Lag 4e+05 0.002808027
## Lag 5e+05 -0.000157396
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.1026 1.5100 0.3990
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.2370 NaN 0.7180
## nodematch.race..wa.O
## -0.1190
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.9182964 0.1310373 0.6899214
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.8126266 NaN 0.4727737
## nodematch.race..wa.O
## 0.9053081
## Joint P-value (lower = worse): 0.5639033 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.7068 0.9596 1.8423
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 1.3662 NaN 0.8566
## nodematch.race..wa.O
## 0.5856
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.08786123 0.33727499 0.06542821
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.17188794 NaN 0.39166285
## nodematch.race..wa.O
## 0.55816312
## Joint P-value (lower = worse): 0.5021129 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.6560 -2.1425 -0.7895
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.2069 NaN -0.6992
## nodematch.race..wa.O
## -1.8992
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.09772466 0.03215543 0.42982988
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.83608788 NaN 0.48444197
## nodematch.race..wa.O
## 0.05753340
## Joint P-value (lower = worse): 0.3762362 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.1273 -0.5726 2.2256
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -0.1670 NaN 0.6328
## nodematch.race..wa.O
## 0.6381
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.25960992 0.56691704 0.02603771
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.86738998 NaN 0.52686028
## nodematch.race..wa.O
## 0.52343552
## Joint P-value (lower = worse): 0.1484366 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.34113 1.10827 1.64845
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -0.76016 NaN 0.59144
## nodematch.race..wa.O
## -0.07205
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7330063 0.2677447 0.0992612
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.4471617 NaN 0.5542288
## nodematch.race..wa.O
## 0.9425598
## Joint P-value (lower = worse): 0.3701823 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.5040 0.7083 1.8790
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.1594 NaN -1.0944
## nodematch.race..wa.O
## 0.6603
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.1325868 0.4787634 0.0602406
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.8733316 NaN 0.2737702
## nodematch.race..wa.O
## 0.5090607
## Joint P-value (lower = worse): 0.3780885 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.09589 -0.89212 -0.13076
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.21724 NaN -1.57929
## nodematch.race..wa.O
## -0.33912
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.9236099 0.3723267 0.8959632
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.8280217 NaN 0.1142703
## nodematch.race..wa.O
## 0.7345201
## Joint P-value (lower = worse): 0.6819906 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.8785 -2.7077 -1.9445
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -0.1622 NaN -0.9914
## nodematch.race..wa.O
## -0.2855
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.37966794 0.00677590 0.05183046
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.87114910 NaN 0.32150661
## nodematch.race..wa.O
## 0.77523173
## Joint P-value (lower = worse): 0.1502384 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 5
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -35.75960 40.288 0.23260 0.23222
## nodefactor.deg.main.1 0.07203 45.393 0.26208 0.26097
## nodefactor.race..wa.B 36.69990 15.670 0.09047 0.09047
## nodefactor.race..wa.H 36.38233 19.962 0.11525 0.11566
## nodefactor.region.EW -0.08000 18.912 0.10919 0.10905
## nodefactor.region.OW 1.07807 36.490 0.21067 0.20794
## nodematch.race..wa.B -8.47681 0.000 0.00000 0.00000
## nodematch.race..wa.H -36.45184 3.805 0.02197 0.02201
## nodematch.race..wa.O 36.98905 33.244 0.19193 0.19264
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -114.000 -63.000 -36.0000 -9.000 44.000
## nodefactor.deg.main.1 -88.000 -31.000 0.0000 30.000 89.000
## nodefactor.race..wa.B 6.835 25.835 36.8352 46.835 67.835
## nodefactor.race..wa.H -1.908 23.092 36.0920 50.092 76.092
## nodefactor.region.EW -36.680 -12.680 -0.6796 12.320 37.320
## nodefactor.region.OW -70.548 -23.548 0.4520 25.452 73.452
## nodematch.race..wa.B -8.477 -8.477 -8.4768 -8.477 -8.477
## nodematch.race..wa.H -43.200 -39.200 -36.1997 -34.200 -28.200
## nodematch.race..wa.O -27.844 14.156 37.1558 59.156 102.156
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges nodefactor.deg.main.1
## edges 1.00000000 0.76054818
## nodefactor.deg.main.1 0.76054818 1.00000000
## nodefactor.race..wa.B 0.35940838 0.24348279
## nodefactor.race..wa.H 0.42896961 0.34432058
## nodefactor.region.EW 0.40354588 0.30746177
## nodefactor.region.OW 0.66468989 0.46226660
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.07902052 0.07157873
## nodematch.race..wa.O 0.79392721 0.60836073
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.359408384 0.428969608
## nodefactor.deg.main.1 0.243482789 0.344320581
## nodefactor.race..wa.B 1.000000000 -0.004141117
## nodefactor.race..wa.H -0.004141117 1.000000000
## nodefactor.region.EW 0.087944757 0.265898248
## nodefactor.region.OW 0.220759040 0.267902595
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.004055651 0.354335329
## nodematch.race..wa.O -0.032842076 -0.038115228
## nodefactor.region.EW nodefactor.region.OW
## edges 0.40354588 0.66468989
## nodefactor.deg.main.1 0.30746177 0.46226660
## nodefactor.race..wa.B 0.08794476 0.22075904
## nodefactor.race..wa.H 0.26589825 0.26790259
## nodefactor.region.EW 1.00000000 0.12370677
## nodefactor.region.OW 0.12370677 1.00000000
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.07120527 0.04788087
## nodematch.race..wa.O 0.29607866 0.54607823
## nodematch.race..wa.B nodematch.race..wa.H
## edges NA 0.079020518
## nodefactor.deg.main.1 NA 0.071578726
## nodefactor.race..wa.B NA 0.004055651
## nodefactor.race..wa.H NA 0.354335329
## nodefactor.region.EW NA 0.071205273
## nodefactor.region.OW NA 0.047880874
## nodematch.race..wa.B 1 NA
## nodematch.race..wa.H NA 1.000000000
## nodematch.race..wa.O NA -0.004468835
## nodematch.race..wa.O
## edges 0.793927212
## nodefactor.deg.main.1 0.608360727
## nodefactor.race..wa.B -0.032842076
## nodefactor.race..wa.H -0.038115228
## nodefactor.region.EW 0.296078656
## nodefactor.region.OW 0.546078228
## nodematch.race..wa.B NA
## nodematch.race..wa.H -0.004468835
## nodematch.race..wa.O 1.000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.006920140 0.0007543802 -0.007726221
## Lag 2e+05 -0.001562634 0.0113863704 -0.003760960
## Lag 3e+05 0.014590096 -0.0020609688 -0.004835891
## Lag 4e+05 0.016158739 0.0087172971 -0.029290893
## Lag 5e+05 -0.018248830 0.0010250134 -0.018565737
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.003247102 0.0008359808 0.002073049
## Lag 2e+05 -0.014925791 0.0242954052 -0.008063595
## Lag 3e+05 -0.017800445 0.0322595483 0.017994280
## Lag 4e+05 0.014905960 -0.0257610793 -0.011515614
## Lag 5e+05 -0.027213326 -0.0057712815 -0.007513095
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.006795651 0.001279202
## Lag 2e+05 NaN 0.024304177 -0.023386609
## Lag 3e+05 NaN -0.019813275 0.022353223
## Lag 4e+05 NaN -0.006022160 0.022087185
## Lag 5e+05 NaN 0.042762637 0.001667387
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000e+00
## Lag 1e+05 -0.006307659 -0.002279447 -8.968465e-03
## Lag 2e+05 -0.007894586 0.007084864 -1.478297e-02
## Lag 3e+05 -0.014990607 -0.029592100 3.166262e-03
## Lag 4e+05 -0.004174385 0.008500037 -1.049888e-03
## Lag 5e+05 0.013633641 0.014201471 -4.773963e-05
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.004092067 0.0028398898 -0.029271045
## Lag 2e+05 0.004982865 -0.0001421547 -0.012415471
## Lag 3e+05 -0.038941511 0.0094561546 -0.033068812
## Lag 4e+05 0.004542032 0.0203550964 0.003825339
## Lag 5e+05 -0.003433483 0.0175285931 0.020344146
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.023040502 0.006601975
## Lag 2e+05 NaN 0.009392134 -0.028008959
## Lag 3e+05 NaN -0.004844628 -0.008093553
## Lag 4e+05 NaN -0.002291789 -0.008972590
## Lag 5e+05 NaN -0.010806979 0.022959398
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.020851445 0.0213449240 0.021002203
## Lag 2e+05 -0.015201339 0.0002802347 -0.005622173
## Lag 3e+05 0.001101881 -0.0009375464 0.007344251
## Lag 4e+05 0.009078094 0.0105087641 0.019429328
## Lag 5e+05 0.007257310 0.0062699208 -0.006188380
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 -0.014542523 -0.0155730882 0.02016988
## Lag 2e+05 0.009116621 0.0007449845 -0.01315595
## Lag 3e+05 -0.001587001 -0.0180583041 0.01369896
## Lag 4e+05 0.011902681 -0.0018120151 0.01221622
## Lag 5e+05 0.021365439 0.0133568239 0.02043736
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.008616910 0.019456869
## Lag 2e+05 NaN 0.004765738 -0.014249017
## Lag 3e+05 NaN 0.021865753 -0.008100761
## Lag 4e+05 NaN -0.022507011 0.001454497
## Lag 5e+05 NaN 0.015920579 0.010156596
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.021892723 0.01308473 0.01998203
## Lag 2e+05 -0.000447345 0.01426082 -0.01112593
## Lag 3e+05 -0.016054171 -0.04103977 -0.01503728
## Lag 4e+05 0.001456830 0.01179998 -0.03268493
## Lag 5e+05 -0.045034925 -0.02580277 -0.01848709
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000e+00 1.000000000 1.000000000
## Lag 1e+05 -1.718532e-02 0.004451530 -0.013626883
## Lag 2e+05 1.308271e-02 -0.005608763 0.009850617
## Lag 3e+05 8.844331e-05 -0.016787812 -0.015148656
## Lag 4e+05 2.248276e-02 0.035707193 0.001338650
## Lag 5e+05 1.086732e-02 0.011477575 -0.032304884
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.005146971 0.025112935
## Lag 2e+05 NaN -0.006337931 -0.001979264
## Lag 3e+05 NaN -0.004903854 0.004386690
## Lag 4e+05 NaN 0.020982723 -0.011081073
## Lag 5e+05 NaN -0.016283933 -0.036330387
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.01100127 -0.002755822 0.020389338
## Lag 2e+05 -0.03394009 -0.018456998 -0.005972124
## Lag 3e+05 -0.00765711 0.007680960 0.013632004
## Lag 4e+05 0.01150824 -0.034443461 0.012439760
## Lag 5e+05 0.01100247 0.011296929 -0.016254922
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.006132266 -0.012508750 0.023840794
## Lag 2e+05 0.006098205 0.022392914 -0.041903018
## Lag 3e+05 -0.020508693 -0.020817556 0.001440925
## Lag 4e+05 -0.010138528 -0.003203452 0.026879674
## Lag 5e+05 0.003348459 0.017626430 0.006804279
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.009551700 0.022784676
## Lag 2e+05 NaN -0.004433462 -0.047491945
## Lag 3e+05 NaN -0.009170469 -0.012520830
## Lag 4e+05 NaN 0.002449292 0.004751113
## Lag 5e+05 NaN 0.018986048 -0.008281171
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.026259261 0.0001329232 -0.008128521
## Lag 2e+05 -0.002028759 0.0195560600 0.008275359
## Lag 3e+05 -0.013039523 0.0037433904 -0.024489054
## Lag 4e+05 0.031290776 0.0236331563 0.007937543
## Lag 5e+05 0.025598242 0.0206769730 0.001854600
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.009392497 0.011288309 -0.004395589
## Lag 2e+05 -0.028477280 -0.034692172 0.003138002
## Lag 3e+05 -0.003354733 -0.005619677 -0.006966524
## Lag 4e+05 0.030775161 0.010482577 0.039623330
## Lag 5e+05 -0.004706714 0.002322290 -0.009895477
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.020124386 0.014251571
## Lag 2e+05 NaN 0.007609056 0.003889284
## Lag 3e+05 NaN 0.019326728 0.002111854
## Lag 4e+05 NaN 0.026975149 0.042443957
## Lag 5e+05 NaN -0.004949391 0.012206703
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.029002108 0.0186322432 -0.02234278
## Lag 2e+05 0.024991968 0.0199677327 0.01727104
## Lag 3e+05 0.004026168 -0.0003743267 0.01659402
## Lag 4e+05 -0.005388663 -0.0005958859 0.01314381
## Lag 5e+05 -0.036499215 -0.0041121585 -0.01070611
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 0.0404599422 0.0226547088 0.003442628
## Lag 2e+05 -0.0083028263 0.0004761757 0.007009784
## Lag 3e+05 -0.0002298428 -0.0006464667 0.007044039
## Lag 4e+05 -0.0118833996 -0.0076669732 0.019547146
## Lag 5e+05 0.0179819985 -0.0279571785 -0.014836038
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.0000000000 1.0000000000
## Lag 1e+05 NaN 0.0071635955 0.0262006842
## Lag 2e+05 NaN -0.0005469372 0.0225552223
## Lag 3e+05 NaN 0.0007284861 -0.0014688764
## Lag 4e+05 NaN -0.0100947523 -0.0003398162
## Lag 5e+05 NaN 0.0206805579 -0.0056920144
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.01566956 0.020796015 0.011766582
## Lag 2e+05 -0.03146633 -0.005563645 -0.013993795
## Lag 3e+05 -0.03098937 -0.036833032 0.018878352
## Lag 4e+05 -0.02663669 0.001597165 -0.015789391
## Lag 5e+05 -0.02482600 -0.004999028 -0.005091651
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.015481640 -0.023957423 -0.0056389735
## Lag 2e+05 0.011727266 0.002944722 -0.0270033556
## Lag 3e+05 -0.041436984 0.006908259 -0.0076949757
## Lag 4e+05 0.004852107 -0.035106985 -0.0002691687
## Lag 5e+05 0.003576914 -0.005835384 -0.0291163356
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.023250011 0.001320633
## Lag 2e+05 NaN -0.017450425 -0.032472670
## Lag 3e+05 NaN -0.014707651 0.001535080
## Lag 4e+05 NaN 0.026714606 -0.030742711
## Lag 5e+05 NaN -0.008641063 -0.005867381
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.407900 0.310278 1.021049
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.057528 -1.252419 0.565962
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -0.917209 -0.006474
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6833471 0.7563497 0.3072314
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9541248 0.2104172 0.5714195
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.3590329 0.9948344
## Joint P-value (lower = worse): 0.9059805 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.81122 1.07426 -0.02656
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.28799 1.75549 0.10587
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -1.41508 0.11947
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.41723758 0.28270758 0.97881418
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.19774985 0.07917537 0.91568218
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.15704416 0.90490508
## Joint P-value (lower = worse): 0.4556301 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.74307 1.79392 -0.65422
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.79928 0.34196 0.00639
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -0.80946 1.77667
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.08132188 0.07282635 0.51297059
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.42413035 0.73238230 0.99490160
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.41824854 0.07562257
## Joint P-value (lower = worse): 0.3374752 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.6132 1.9608 -0.5088
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9554 1.7994 -0.4040
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.7759 1.6318
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.10669816 0.04989781 0.61086855
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.33935317 0.07196071 0.68617668
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.43777936 0.10272718
## Joint P-value (lower = worse): 0.2417018 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.72487 0.32631 -0.77385
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.45828 -1.18819 -0.70363
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.04267 -0.20822
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.4685301 0.7441868 0.4390219
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.6467527 0.2347598 0.4816640
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.9659669 0.8350552
## Joint P-value (lower = worse): 0.8868549 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.4980 -1.5332 -1.3485
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.6193 1.8091 -0.7740
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -0.4237 -0.5529
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.61845051 0.12521699 0.17748197
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.53574750 0.07043814 0.43890678
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.67176905 0.58034299
## Joint P-value (lower = worse): 0.2908849 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.53914 -0.15908 -1.03425
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.08188 1.19952 0.63381
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 1.53474 0.01412
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5897928 0.8736062 0.3010204
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9347394 0.2303240 0.5262057
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.1248471 0.9887326
## Joint P-value (lower = worse): 0.3727964 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.38699 -0.46981 -0.92253
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.07887 -1.29674 0.23852
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -1.32746 -0.11240
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6987641 0.6384942 0.3562497
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9371392 0.1947198 0.8114782
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.1843551 0.9105094
## Joint P-value (lower = worse): 0.7843281 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 6
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -37.3340 39.919 0.23047 0.23142
## nodefactor.deg.main.1 -0.8270 45.266 0.26134 0.26135
## nodefactor.race..wa.B 36.3643 15.695 0.09061 0.08997
## nodefactor.race..wa.H 36.6297 20.025 0.11561 0.11536
## nodefactor.region.EW 0.1932 18.887 0.10905 0.10807
## nodefactor.region.OW -1.4275 36.680 0.21177 0.21177
## nodematch.race..wa.B -8.4768 0.000 0.00000 0.00000
## nodematch.race..wa.H -36.3424 3.825 0.02208 0.02226
## nodematch.race..wa.O 35.6122 32.858 0.18970 0.18954
## absdiff.sqrt.age 0.3369 45.584 0.26318 0.26318
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -116.000 -64.000 -37.0000 -10.000 41.000
## nodefactor.deg.main.1 -89.000 -31.000 -1.0000 30.000 88.000
## nodefactor.race..wa.B 5.835 25.835 35.8352 46.835 66.835
## nodefactor.race..wa.H -1.908 23.092 36.0920 50.092 76.092
## nodefactor.region.EW -36.680 -12.680 0.3204 12.320 38.320
## nodefactor.region.OW -72.548 -26.548 -1.5480 23.452 69.452
## nodematch.race..wa.B -8.477 -8.477 -8.4768 -8.477 -8.477
## nodematch.race..wa.H -43.200 -39.200 -36.1997 -34.200 -28.200
## nodematch.race..wa.O -27.844 13.156 35.1558 58.156 100.156
## absdiff.sqrt.age -88.949 -30.698 0.3668 31.252 89.851
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges nodefactor.deg.main.1
## edges 1.00000000 0.75772330
## nodefactor.deg.main.1 0.75772330 1.00000000
## nodefactor.race..wa.B 0.35646179 0.23583839
## nodefactor.race..wa.H 0.43391070 0.35007220
## nodefactor.region.EW 0.39494391 0.29726957
## nodefactor.region.OW 0.66870232 0.46884798
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.08253419 0.06452848
## nodematch.race..wa.O 0.78979489 0.60206914
## absdiff.sqrt.age 0.73908669 0.55921788
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.35646179 0.43391070
## nodefactor.deg.main.1 0.23583839 0.35007220
## nodefactor.race..wa.B 1.00000000 -0.01155710
## nodefactor.race..wa.H -0.01155710 1.00000000
## nodefactor.region.EW 0.07911717 0.27202956
## nodefactor.region.OW 0.22397885 0.27496522
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.01201325 0.33918919
## nodematch.race..wa.O -0.03615516 -0.03727785
## absdiff.sqrt.age 0.26569426 0.32306807
## nodefactor.region.EW nodefactor.region.OW
## edges 0.39494391 0.66870232
## nodefactor.deg.main.1 0.29726957 0.46884798
## nodefactor.race..wa.B 0.07911717 0.22397885
## nodefactor.race..wa.H 0.27202956 0.27496522
## nodefactor.region.EW 1.00000000 0.12013726
## nodefactor.region.OW 0.12013726 1.00000000
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.06931307 0.05450349
## nodematch.race..wa.O 0.28430823 0.54418877
## absdiff.sqrt.age 0.28438448 0.49709396
## nodematch.race..wa.B nodematch.race..wa.H
## edges NA 0.082534195
## nodefactor.deg.main.1 NA 0.064528481
## nodefactor.race..wa.B NA 0.012013254
## nodefactor.race..wa.H NA 0.339189187
## nodefactor.region.EW NA 0.069313070
## nodefactor.region.OW NA 0.054503486
## nodematch.race..wa.B 1 NA
## nodematch.race..wa.H NA 1.000000000
## nodematch.race..wa.O NA 0.004223227
## absdiff.sqrt.age NA 0.060399431
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.789794893 0.73908669
## nodefactor.deg.main.1 0.602069142 0.55921788
## nodefactor.race..wa.B -0.036155157 0.26569426
## nodefactor.race..wa.H -0.037277854 0.32306807
## nodefactor.region.EW 0.284308226 0.28438448
## nodefactor.region.OW 0.544188773 0.49709396
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.004223227 0.06039943
## nodematch.race..wa.O 1.000000000 0.58114329
## absdiff.sqrt.age 0.581143292 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000e+00
## Lag 1e+05 0.001180621 -0.006334552 -7.751064e-05
## Lag 2e+05 -0.005495008 -0.007128924 1.047376e-02
## Lag 3e+05 -0.011202157 -0.022554241 9.792288e-03
## Lag 4e+05 -0.004633560 0.017138440 -1.446275e-02
## Lag 5e+05 0.018662549 0.025788613 8.652062e-03
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.006491597 -0.006710416 0.007916870
## Lag 2e+05 0.008979006 -0.005096989 -0.003725218
## Lag 3e+05 -0.004130969 -0.036570794 -0.010402986
## Lag 4e+05 -0.008232362 0.020522750 -0.004969289
## Lag 5e+05 0.014750679 0.004755628 -0.016866358
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.0000000000
## Lag 1e+05 NaN 0.003836159 -0.0130444790
## Lag 2e+05 NaN 0.044524794 -0.0011114488
## Lag 3e+05 NaN 0.034205413 0.0067570291
## Lag 4e+05 NaN -0.011970927 -0.0007641154
## Lag 5e+05 NaN -0.011838533 0.0057273655
## absdiff.sqrt.age
## Lag 0 1.0000000000
## Lag 1e+05 0.0144129601
## Lag 2e+05 -0.0091254866
## Lag 3e+05 -0.0063831598
## Lag 4e+05 0.0013312372
## Lag 5e+05 0.0005517172
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.012912010 -0.0082080567 0.0110533897
## Lag 2e+05 -0.015934359 -0.0003417295 -0.0008891257
## Lag 3e+05 0.009705569 -0.0092575436 0.0234499021
## Lag 4e+05 -0.012405746 -0.0127103253 0.0299248014
## Lag 5e+05 -0.009162732 -0.0056856012 0.0143443579
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 -0.022852357 -0.001724069 -0.01353271
## Lag 2e+05 -0.003198268 -0.003385084 -0.00016209
## Lag 3e+05 0.008252763 -0.007967320 0.01529012
## Lag 4e+05 -0.001051809 0.011779988 -0.01570769
## Lag 5e+05 0.007581213 -0.004883294 -0.01764065
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.004885881 -0.022121515
## Lag 2e+05 NaN 0.002159978 -0.019081114
## Lag 3e+05 NaN -0.011924880 0.035227439
## Lag 4e+05 NaN 0.005658210 -0.003669075
## Lag 5e+05 NaN 0.002027072 -0.019524938
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.007961599
## Lag 2e+05 -0.003763338
## Lag 3e+05 0.013104194
## Lag 4e+05 -0.006292490
## Lag 5e+05 -0.014121989
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0149773221 -0.006537942 -0.022102789
## Lag 2e+05 0.0077752232 -0.020096844 0.021053704
## Lag 3e+05 0.0004472099 -0.013254436 -0.006449229
## Lag 4e+05 0.0080082787 -0.021173883 -0.012994095
## Lag 5e+05 -0.0079862478 -0.017027930 0.036040640
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 -0.00273928 -0.002263771 -0.003402030
## Lag 2e+05 -0.01367584 0.005588768 0.016133681
## Lag 3e+05 0.01218998 0.027117655 -0.006525887
## Lag 4e+05 0.02514214 -0.006989485 0.029786577
## Lag 5e+05 -0.01614954 -0.016563445 -0.003125664
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.013414263 -0.004911775
## Lag 2e+05 NaN -0.012504768 0.016389496
## Lag 3e+05 NaN -0.004550009 0.005713338
## Lag 4e+05 NaN 0.011007036 -0.003797593
## Lag 5e+05 NaN 0.020038116 -0.034822330
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.015724482
## Lag 2e+05 -0.006246153
## Lag 3e+05 0.010622559
## Lag 4e+05 -0.007835825
## Lag 5e+05 0.004464919
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.00000000
## Lag 1e+05 -0.0056979711 -0.002851530 0.03864994
## Lag 2e+05 -0.0052584088 -0.011004537 -0.00143329
## Lag 3e+05 0.0141416703 0.014538062 -0.01025144
## Lag 4e+05 0.0008867851 -0.009513295 -0.00914985
## Lag 5e+05 -0.0147437168 -0.030827409 0.03281718
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.024034416 0.020854945 0.006740192
## Lag 2e+05 -0.001505770 0.013143455 0.006005972
## Lag 3e+05 0.017519357 0.003655047 0.017343411
## Lag 4e+05 -0.008112655 0.028892163 -0.036429019
## Lag 5e+05 -0.008609845 0.003884061 -0.006683451
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.0000000000 1.000000000
## Lag 1e+05 NaN 0.0005575539 0.004407273
## Lag 2e+05 NaN -0.0181172212 0.004507011
## Lag 3e+05 NaN 0.0326183613 0.010708062
## Lag 4e+05 NaN -0.0207992325 0.001271791
## Lag 5e+05 NaN -0.0122747376 -0.008473650
## absdiff.sqrt.age
## Lag 0 1.0000000000
## Lag 1e+05 -0.0116643826
## Lag 2e+05 -0.0176631865
## Lag 3e+05 0.0140838151
## Lag 4e+05 0.0007270239
## Lag 5e+05 -0.0153794710
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 -0.005013070 -0.004014229 -0.02389439
## Lag 2e+05 -0.002397745 0.008417129 -0.02019361
## Lag 3e+05 -0.007176281 0.021247209 0.01179908
## Lag 4e+05 -0.014252559 -0.014550921 -0.03751960
## Lag 5e+05 0.009642238 0.006491331 -0.01228158
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.004324964 0.004980467 0.009341976
## Lag 2e+05 0.002422719 -0.011358198 0.025882355
## Lag 3e+05 0.003402381 0.014208037 -0.010361145
## Lag 4e+05 0.012343520 -0.019335307 -0.031341414
## Lag 5e+05 0.007667633 0.007663114 0.001596306
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.001021210 0.004312474
## Lag 2e+05 NaN -0.034941402 -0.003642870
## Lag 3e+05 NaN -0.016936044 -0.012128086
## Lag 4e+05 NaN 0.012309230 -0.008052318
## Lag 5e+05 NaN 0.008219758 0.004607952
## absdiff.sqrt.age
## Lag 0 1.0000000000
## Lag 1e+05 -0.0006528685
## Lag 2e+05 0.0038058520
## Lag 3e+05 -0.0195058440
## Lag 4e+05 -0.0115658915
## Lag 5e+05 0.0079897421
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.0025884725 -0.0140488990 0.0005955699
## Lag 2e+05 -0.0127109683 -0.0184398164 0.0031510305
## Lag 3e+05 0.0044891171 0.0002638759 0.0199405883
## Lag 4e+05 0.0006152233 -0.0029112754 0.0038404384
## Lag 5e+05 0.0249596607 0.0094479002 0.0011165588
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.012323604 -0.002136137 -0.015383430
## Lag 2e+05 0.001168489 0.010575831 -0.012683980
## Lag 3e+05 -0.014349899 -0.004907547 -0.013341649
## Lag 4e+05 -0.019413873 0.020792408 0.002532154
## Lag 5e+05 0.013116229 -0.003251437 0.021342811
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.0000000000 1.0000000000
## Lag 1e+05 NaN 0.0398067104 0.0008116609
## Lag 2e+05 NaN 0.0046019488 0.0036796228
## Lag 3e+05 NaN -0.0346775115 0.0056297852
## Lag 4e+05 NaN 0.0001525984 0.0055476295
## Lag 5e+05 NaN 0.0010864826 0.0127447322
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.001946528
## Lag 2e+05 -0.000171540
## Lag 3e+05 0.002327198
## Lag 4e+05 -0.027144308
## Lag 5e+05 -0.007599620
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.006838309 -0.0004407087 0.026043904
## Lag 2e+05 0.021659020 0.0028538371 0.017137087
## Lag 3e+05 -0.029673243 -0.0075366136 0.001859680
## Lag 4e+05 0.006267427 -0.0421948094 -0.034964980
## Lag 5e+05 0.002657771 -0.0256905512 -0.005743912
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.010735651 -0.013148143 0.020942633
## Lag 2e+05 -0.017731373 -0.012725009 0.003963117
## Lag 3e+05 -0.028333577 -0.050872935 0.002998686
## Lag 4e+05 0.017440135 -0.008861462 -0.006893772
## Lag 5e+05 0.006833705 0.016905315 -0.012018564
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.0000000000
## Lag 1e+05 NaN -0.008689809 -0.0123325912
## Lag 2e+05 NaN 0.022216995 0.0162053362
## Lag 3e+05 NaN -0.022174708 -0.0217311058
## Lag 4e+05 NaN -0.015118393 -0.0006478394
## Lag 5e+05 NaN 0.021451594 0.0124007020
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.016498205
## Lag 2e+05 0.012050335
## Lag 3e+05 -0.015261170
## Lag 4e+05 0.009813626
## Lag 5e+05 0.020198891
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.006062183 -0.015678086 0.0083105430
## Lag 2e+05 0.006708011 -0.024301796 0.0069158047
## Lag 3e+05 0.029722364 0.014832743 -0.0118971343
## Lag 4e+05 -0.018904125 0.001544208 -0.0218237872
## Lag 5e+05 0.001799924 0.019207273 0.0004042609
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.044219042 -0.020547888 0.012621231
## Lag 2e+05 0.023860559 -0.004274842 0.011252962
## Lag 3e+05 -0.009494200 0.009675042 0.018294448
## Lag 4e+05 0.019187717 -0.028486311 -0.005127968
## Lag 5e+05 -0.001881202 0.021546537 0.015994079
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.020244943 -0.018601924
## Lag 2e+05 NaN 0.012116947 0.003664529
## Lag 3e+05 NaN -0.005738144 0.029644200
## Lag 4e+05 NaN -0.009694277 -0.004178620
## Lag 5e+05 NaN -0.009737966 -0.015342212
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.007822104
## Lag 2e+05 0.005986246
## Lag 3e+05 -0.006654198
## Lag 4e+05 0.022017511
## Lag 5e+05 -0.010208184
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.3175 -2.0607 0.1804
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.4403 -0.5961 -1.7997
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -0.2008 -0.9201
## absdiff.sqrt.age
## -0.6460
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.18767872 0.03932994 0.85682794
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.14977960 0.55107791 0.07190995
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.84084621 0.35751807
## absdiff.sqrt.age
## 0.51828309
## Joint P-value (lower = worse): 0.7923288 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.34226 0.89225 -0.63897
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.23474 0.01799 0.08582
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -0.66994 0.51600
## absdiff.sqrt.age
## -0.47904
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7321528 0.3722564 0.5228429
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8144100 0.9856458 0.9316080
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.5028960 0.6058556
## absdiff.sqrt.age
## 0.6319123
## Joint P-value (lower = worse): 0.9414097 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.6991 0.1333 1.2808
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5350 1.6995 2.0221
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -1.5407 1.0108
## absdiff.sqrt.age
## 1.0473
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.08929739 0.89396000 0.20027690
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.59268025 0.08922286 0.04316501
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.12338284 0.31210347
## absdiff.sqrt.age
## 0.29493917
## Joint P-value (lower = worse): 0.2237952 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.02243 0.38552 1.68264
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -2.61048 -0.25985 -1.70163
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -1.62649 0.49682
## absdiff.sqrt.age
## -1.26781
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.982107172 0.699853163 0.092444530
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.009041569 0.794980741 0.088825395
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.103845754 0.619314892
## absdiff.sqrt.age
## 0.204866536
## Joint P-value (lower = worse): 0.05389209 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.33619 1.08926 0.12498
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.64254 0.62688 0.72110
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -0.66717 1.05827
## absdiff.sqrt.age
## -0.01831
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.1814856 0.2760386 0.9005356
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5205229 0.5307352 0.4708479
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.5046646 0.2899331
## absdiff.sqrt.age
## 0.9853903
## Joint P-value (lower = worse): 0.8622414 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.167988 1.827731 -1.512398
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.711738 1.232957 1.314179
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.005047 1.636631
## absdiff.sqrt.age
## 0.143740
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.24281160 0.06758992 0.13043257
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.47662729 0.21759175 0.18878596
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.99597306 0.10170767
## absdiff.sqrt.age
## 0.88570617
## Joint P-value (lower = worse): 0.2610809 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.8070 -1.4013 -2.1040
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.5143 -0.2389 -0.1297
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.3610 0.4783
## absdiff.sqrt.age
## -0.3008
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.41967348 0.16111525 0.03537669
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.60706434 0.81119483 0.89678637
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.71812472 0.63243793
## absdiff.sqrt.age
## 0.76359452
## Joint P-value (lower = worse): 0.5127734 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.9379 -2.1068 0.2124
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.5172 -0.9660 -0.6145
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.7762 -2.0746
## absdiff.sqrt.age
## -0.4365
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.05263505 0.03513120 0.83183241
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.60501069 0.33402658 0.53886321
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.43765545 0.03802777
## absdiff.sqrt.age
## 0.66248329
## Joint P-value (lower = worse): 0.3984074 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 7
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -35.47483 59.715 0.34476 0.36818
## nodefactor.deg.main.1 0.08127 61.932 0.35757 0.38070
## nodefactor.race..wa.B 36.74380 19.290 0.11137 0.11873
## nodefactor.race..wa.H 36.61330 24.968 0.14415 0.14912
## nodefactor.region.EW 0.10143 23.859 0.13775 0.14432
## nodefactor.region.OW 0.31780 48.874 0.28217 0.29177
## concurrent 0.70853 53.712 0.31010 0.33271
## nodematch.race..wa.B -8.47681 0.000 0.00000 0.00000
## nodematch.race..wa.H -36.42757 3.923 0.02265 0.02327
## nodematch.race..wa.O 37.02321 45.238 0.26118 0.27725
## absdiff.sqrt.age 0.71055 59.491 0.34347 0.36462
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -152.0000 -76.000 -3.600e+01 5.000 83.000
## nodefactor.deg.main.1 -120.0000 -42.000 0.000e+00 41.000 123.000
## nodefactor.race..wa.B -0.1648 23.835 3.684e+01 49.835 74.835
## nodefactor.race..wa.H -10.9080 19.092 3.709e+01 53.092 86.092
## nodefactor.region.EW -45.6796 -15.680 3.204e-01 16.320 47.320
## nodefactor.region.OW -93.5480 -32.548 4.520e-01 32.452 97.452
## concurrent -103.0000 -36.000 -2.274e-13 37.000 107.000
## nodematch.race..wa.B -8.4768 -8.477 -8.477e+00 -8.477 -8.477
## nodematch.race..wa.H -43.1997 -39.200 -3.620e+01 -34.200 -28.200
## nodematch.race..wa.O -51.8442 6.156 3.716e+01 67.156 126.181
## absdiff.sqrt.age -113.7449 -39.763 1.380e-01 40.465 118.729
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges nodefactor.deg.main.1
## edges 1.0000000 0.8232231
## nodefactor.deg.main.1 0.8232231 1.0000000
## nodefactor.race..wa.B 0.4389905 0.3290299
## nodefactor.race..wa.H 0.5167149 0.4412531
## nodefactor.region.EW 0.4795969 0.4017143
## nodefactor.region.OW 0.7422210 0.5828574
## concurrent 0.9582711 0.7876116
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.1159713 0.1037311
## nodematch.race..wa.O 0.8576908 0.7118194
## absdiff.sqrt.age 0.8515697 0.6991526
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.438990520 0.51671488
## nodefactor.deg.main.1 0.329029942 0.44125308
## nodefactor.race..wa.B 1.000000000 0.04844203
## nodefactor.race..wa.H 0.048442027 1.00000000
## nodefactor.region.EW 0.163517146 0.34220171
## nodefactor.region.OW 0.303603143 0.37304485
## concurrent 0.423846564 0.49404754
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.004871687 0.40019043
## nodematch.race..wa.O 0.126746326 0.14418612
## absdiff.sqrt.age 0.375457333 0.44207943
## nodefactor.region.EW nodefactor.region.OW concurrent
## edges 0.4795969 0.74222099 0.9582711
## nodefactor.deg.main.1 0.4017143 0.58285744 0.7876116
## nodefactor.race..wa.B 0.1635171 0.30360314 0.4238466
## nodefactor.race..wa.H 0.3422017 0.37304485 0.4940475
## nodefactor.region.EW 1.0000000 0.22857346 0.4568735
## nodefactor.region.OW 0.2285735 1.00000000 0.7066057
## concurrent 0.4568735 0.70660575 1.0000000
## nodematch.race..wa.B NA NA NA
## nodematch.race..wa.H 0.1043839 0.08495395 0.1132865
## nodematch.race..wa.O 0.3835298 0.65175550 0.8213435
## absdiff.sqrt.age 0.3992235 0.63144313 0.8124875
## nodematch.race..wa.B nodematch.race..wa.H
## edges NA 0.115971271
## nodefactor.deg.main.1 NA 0.103731125
## nodefactor.race..wa.B NA 0.004871687
## nodefactor.race..wa.H NA 0.400190428
## nodefactor.region.EW NA 0.104383944
## nodefactor.region.OW NA 0.084953954
## concurrent NA 0.113286520
## nodematch.race..wa.B 1 NA
## nodematch.race..wa.H NA 1.000000000
## nodematch.race..wa.O NA 0.016838978
## absdiff.sqrt.age NA 0.095243622
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.85769081 0.85156967
## nodefactor.deg.main.1 0.71181936 0.69915264
## nodefactor.race..wa.B 0.12674633 0.37545733
## nodefactor.race..wa.H 0.14418612 0.44207943
## nodefactor.region.EW 0.38352982 0.39922349
## nodefactor.region.OW 0.65175550 0.63144313
## concurrent 0.82134351 0.81248755
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.01683898 0.09524362
## nodematch.race..wa.O 1.00000000 0.72824792
## absdiff.sqrt.age 0.72824792 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.08077509 0.05113659 0.085527807
## Lag 2e+05 0.01330447 0.01793462 -0.010421904
## Lag 3e+05 0.04236702 0.04801589 -0.012435669
## Lag 4e+05 -0.00713260 0.02806275 -0.006880992
## Lag 5e+05 0.02815143 0.03531967 0.007366477
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0464385762 0.041252377 0.045540577
## Lag 2e+05 0.0133772262 0.015429696 -0.009230566
## Lag 3e+05 0.0009941337 -0.015621490 0.020869210
## Lag 4e+05 0.0104713530 -0.004984904 -0.018109919
## Lag 5e+05 0.0075032513 0.011746854 0.028070267
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 NaN 1.0000000000
## Lag 1e+05 0.09139123 NaN -0.0066558523
## Lag 2e+05 0.01931918 NaN -0.0154258131
## Lag 3e+05 0.03398606 NaN -0.0057995023
## Lag 4e+05 -0.01051710 NaN -0.0008811985
## Lag 5e+05 0.02455403 NaN 0.0098880972
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.062112778 0.0381992309
## Lag 2e+05 0.009279785 0.0112810549
## Lag 3e+05 0.046929187 0.0428568832
## Lag 4e+05 0.014995513 -0.0005664862
## Lag 5e+05 0.031414809 0.0493078320
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0671009349 0.054166048 0.076364629
## Lag 2e+05 0.0211990775 0.008002542 0.013247647
## Lag 3e+05 0.0092064357 0.008621436 -0.003797904
## Lag 4e+05 0.0094416617 0.004339443 0.012819059
## Lag 5e+05 -0.0007135301 -0.011401890 -0.013212008
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.068797658 0.050222674 0.06964077
## Lag 2e+05 -0.005332033 0.005305532 0.02523226
## Lag 3e+05 0.009512394 -0.004124216 -0.01336901
## Lag 4e+05 -0.005891607 0.007450193 0.00900467
## Lag 5e+05 0.002494312 0.030389079 0.01195871
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 NaN 1.000000000
## Lag 1e+05 0.0615320003 NaN 0.008648500
## Lag 2e+05 0.0131933863 NaN 0.002363327
## Lag 3e+05 0.0145662562 NaN -0.021144796
## Lag 4e+05 -0.0001028329 NaN 0.004605311
## Lag 5e+05 -0.0117710861 NaN -0.006542717
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000e+00 1.0000000000
## Lag 1e+05 5.828953e-02 0.0413255793
## Lag 2e+05 1.669416e-02 0.0296014703
## Lag 3e+05 -3.045154e-04 0.0151323854
## Lag 4e+05 -8.097977e-04 0.0185442568
## Lag 5e+05 7.793517e-05 0.0002339497
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.077696836 0.046630989 0.058127699
## Lag 2e+05 -0.025724814 0.015663793 -0.004647695
## Lag 3e+05 0.002623648 -0.011594296 -0.003597631
## Lag 4e+05 0.003123766 -0.001879833 -0.009278866
## Lag 5e+05 0.014516481 0.011752878 -0.035606859
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.046352258 0.04437786 0.061165180
## Lag 2e+05 0.023465073 -0.01159443 -0.012776914
## Lag 3e+05 0.009716968 -0.01642283 -0.005951031
## Lag 4e+05 -0.001017426 0.01204753 0.016178097
## Lag 5e+05 0.016503382 0.02560360 0.028320028
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 0.074654952 NaN 0.006519612
## Lag 2e+05 -0.020973543 NaN 0.033600248
## Lag 3e+05 0.009058755 NaN 0.002376476
## Lag 4e+05 0.005957942 NaN 0.004837043
## Lag 5e+05 0.012842164 NaN 0.002099117
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000000 1.0000000000
## Lag 1e+05 0.0591780560 0.0395546688
## Lag 2e+05 -0.0182799740 -0.0242549062
## Lag 3e+05 -0.0023655605 -0.0005791896
## Lag 4e+05 0.0005461984 0.0132921883
## Lag 5e+05 0.0118854513 0.0064418277
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.044738477 0.040947178 0.084440683
## Lag 2e+05 0.002884406 0.016620349 0.001189156
## Lag 3e+05 0.019576979 0.012757643 -0.009502296
## Lag 4e+05 -0.006912166 -0.002845044 0.012764862
## Lag 5e+05 0.020142443 0.026003441 -0.018994340
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.047571280 0.038274072 0.019788329
## Lag 2e+05 0.012631999 -0.004459196 -0.011720044
## Lag 3e+05 0.021484150 0.037634586 -0.006519736
## Lag 4e+05 0.005500457 0.009080247 0.005751811
## Lag 5e+05 -0.019368217 -0.018070103 0.034949691
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 0.043831741 NaN 0.029995688
## Lag 2e+05 0.009874142 NaN 0.003121192
## Lag 3e+05 0.015340495 NaN -0.015125952
## Lag 4e+05 -0.008939917 NaN 0.001401034
## Lag 5e+05 0.027568780 NaN -0.017390746
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.026753737 0.03062275
## Lag 2e+05 0.007933508 -0.01228077
## Lag 3e+05 0.005083573 0.01681077
## Lag 4e+05 0.007429634 -0.01686335
## Lag 5e+05 0.024759211 0.01151237
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.039615260 0.0377373270 0.045646159
## Lag 2e+05 0.002749847 0.0146719501 0.014708912
## Lag 3e+05 -0.030845677 -0.0197613622 -0.009769839
## Lag 4e+05 -0.043227288 -0.0336113163 0.008155109
## Lag 5e+05 -0.009303895 -0.0003519172 0.013477730
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000e+00 1.000000000
## Lag 1e+05 -0.002300675 4.096343e-02 0.032032596
## Lag 2e+05 0.011573594 -4.587212e-05 -0.003868606
## Lag 3e+05 -0.011789778 -1.213016e-02 0.005509995
## Lag 4e+05 -0.002942026 -2.304804e-02 -0.007092339
## Lag 5e+05 -0.008286256 -1.612553e-02 -0.003085168
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 NaN 1.000000000
## Lag 1e+05 0.0465667959 NaN 0.008187257
## Lag 2e+05 0.0185101848 NaN -0.016341321
## Lag 3e+05 -0.0271680190 NaN -0.006584918
## Lag 4e+05 -0.0510129987 NaN -0.012571578
## Lag 5e+05 -0.0007495809 NaN 0.002402709
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000e+00
## Lag 1e+05 0.037120885 2.964768e-02
## Lag 2e+05 0.019716109 7.075785e-05
## Lag 3e+05 -0.018726892 -2.113334e-02
## Lag 4e+05 -0.024378057 3.918652e-03
## Lag 5e+05 -0.009646746 -1.568663e-02
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.03510402 0.045241680 0.07707384
## Lag 2e+05 0.02222387 0.016612169 0.01998713
## Lag 3e+05 -0.01789309 -0.008707663 -0.02245937
## Lag 4e+05 -0.02997694 0.020969182 -0.04091300
## Lag 5e+05 0.01230665 0.009793536 -0.01032447
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0336637287 0.034956860 0.0036040378
## Lag 2e+05 -0.0335325890 0.006817437 0.0145603565
## Lag 3e+05 -0.0212705984 -0.002997783 -0.0004607675
## Lag 4e+05 -0.0001777812 -0.012700251 -0.0017717964
## Lag 5e+05 0.0047181295 -0.013151350 -0.0007571268
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 NaN 1.0000000000
## Lag 1e+05 0.03161926 NaN 0.0077456003
## Lag 2e+05 0.01616274 NaN 0.0207564724
## Lag 3e+05 -0.01450608 NaN 0.0054408026
## Lag 4e+05 -0.02565314 NaN 0.0001642485
## Lag 5e+05 0.01616452 NaN -0.0199226353
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.010272625 0.005485509
## Lag 2e+05 0.024179288 0.037192089
## Lag 3e+05 -0.027057817 -0.001482983
## Lag 4e+05 -0.011931759 -0.030545251
## Lag 5e+05 0.003060226 0.020255973
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.058893469 0.039965270 0.0970508984
## Lag 2e+05 -0.003261214 -0.005838929 -0.0202830320
## Lag 3e+05 -0.010356870 -0.022393907 0.0009710117
## Lag 4e+05 -0.030328372 -0.018065643 0.0142815565
## Lag 5e+05 0.005329400 0.003885156 0.0268060597
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.025593257 0.028953242 0.037749161
## Lag 2e+05 -0.029328941 0.007382991 -0.003384296
## Lag 3e+05 0.009835422 -0.019876383 0.021210853
## Lag 4e+05 0.014966574 -0.010259903 -0.029336501
## Lag 5e+05 0.011861057 0.005378481 -0.019210676
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 0.062691470 NaN 0.035145579
## Lag 2e+05 -0.006684255 NaN 0.023379840
## Lag 3e+05 -0.010898026 NaN -0.001962925
## Lag 4e+05 -0.034322299 NaN 0.035395273
## Lag 5e+05 0.013456784 NaN -0.010391922
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0485916011 0.028650903
## Lag 2e+05 0.0083355466 -0.004286094
## Lag 3e+05 -0.0045569884 -0.012022407
## Lag 4e+05 -0.0276754105 -0.030301087
## Lag 5e+05 -0.0004347379 0.004342911
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.063519282 0.058167058 0.058256080
## Lag 2e+05 0.024724893 0.032191421 -0.012554003
## Lag 3e+05 -0.024553261 -0.014089360 -0.005774850
## Lag 4e+05 0.005291724 0.009323147 0.025345020
## Lag 5e+05 0.012365026 0.019536362 0.005907464
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 0.0592417955 0.0580704770 0.045737900
## Lag 2e+05 0.0236312583 0.0036273837 -0.002437791
## Lag 3e+05 0.0006360558 -0.0018159307 -0.021784404
## Lag 4e+05 0.0005667996 -0.0022437904 -0.018677511
## Lag 5e+05 -0.0120207749 -0.0003718623 0.027795001
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 0.071652696 NaN 0.025356743
## Lag 2e+05 0.028727811 NaN 0.024539468
## Lag 3e+05 -0.027932774 NaN 0.005422581
## Lag 4e+05 -0.001143187 NaN -0.001770757
## Lag 5e+05 0.015362562 NaN 0.006658790
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0441475904 0.035370401
## Lag 2e+05 0.0254787777 0.009232802
## Lag 3e+05 -0.0118251284 -0.018209615
## Lag 4e+05 0.0007849232 0.015249365
## Lag 5e+05 0.0138695845 0.004689952
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.31929 0.29645 -0.57133
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.83368 0.05405 -0.18778
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.20974 NaN 0.49466
## nodematch.race..wa.O absdiff.sqrt.age
## 1.04046 0.56691
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7495035 0.7668873 0.5677749
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.4044636 0.9568969 0.8510464
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.8338745 NaN 0.6208423
## nodematch.race..wa.O absdiff.sqrt.age
## 0.2981243 0.5707730
## Joint P-value (lower = worse): 0.8516579 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.13193 0.39303 0.26194
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.21982 1.17096 1.57711
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.91553 NaN 0.09126
## nodematch.race..wa.O absdiff.sqrt.age
## 0.82483 0.90333
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.2576630 0.6942963 0.7933644
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.2225344 0.2416153 0.1147704
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.3599129 NaN 0.9272884
## nodematch.race..wa.O absdiff.sqrt.age
## 0.4094671 0.3663501
## Joint P-value (lower = worse): 0.7071803 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.05769 -0.06097 -0.87645
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.50540 0.26183 0.40963
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.41886 NaN 0.39725
## nodematch.race..wa.O absdiff.sqrt.age
## 0.85446 0.05290
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.9539982 0.9513828 0.3807877
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.6132747 0.7934488 0.6820808
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.6753189 NaN 0.6911840
## nodematch.race..wa.O absdiff.sqrt.age
## 0.3928501 0.9578117
## Joint P-value (lower = worse): 0.8182979 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.159390 0.407568 0.105578
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.001864 1.235550 -0.071216
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.479604 NaN 0.170364
## nodematch.race..wa.O absdiff.sqrt.age
## 0.179313 1.562015
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8733618 0.6835910 0.9159174
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9985128 0.2166260 0.9432261
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.6315091 NaN 0.8647241
## nodematch.race..wa.O absdiff.sqrt.age
## 0.8576920 0.1182843
## Joint P-value (lower = worse): 0.2241559 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 2.0429 2.3075 1.5855
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.2320 0.9142 1.5249
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 2.0976 NaN -0.3089
## nodematch.race..wa.O absdiff.sqrt.age
## 1.6555 1.0748
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.04106374 0.02102804 0.11286353
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.81653614 0.36063357 0.12728410
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.03594265 NaN 0.75742239
## nodematch.race..wa.O absdiff.sqrt.age
## 0.09782292 0.28244962
## Joint P-value (lower = worse): 0.5779967 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.80306 0.28290 -0.50234
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.85255 0.25453 -0.21654
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.56866 NaN 0.03293
## nodematch.race..wa.O absdiff.sqrt.age
## 0.84810 0.75193
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.4219413 0.7772502 0.6154269
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3939072 0.7990827 0.8285652
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.5695842 NaN 0.9737299
## nodematch.race..wa.O absdiff.sqrt.age
## 0.3963814 0.4520955
## Joint P-value (lower = worse): 0.8319107 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 2.0072 1.6255 0.7333
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3374 1.2726 1.2668
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 1.8125 NaN -1.7645
## nodematch.race..wa.O absdiff.sqrt.age
## 1.9419 0.7377
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.04472874 0.10404638 0.46336318
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.73580166 0.20314824 0.20521447
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.06991079 NaN 0.07765521
## nodematch.race..wa.O absdiff.sqrt.age
## 0.05214668 0.46067462
## Joint P-value (lower = worse): 0.2015491 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.19740 0.44517 -0.33093
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.59032 1.95262 0.13172
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.26815 NaN 1.43111
## nodematch.race..wa.O absdiff.sqrt.age
## 0.82471 -0.06027
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8435119 0.6561966 0.7406963
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5549772 0.0508651 0.8952041
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.7885858 NaN 0.1523987
## nodematch.race..wa.O absdiff.sqrt.age
## 0.4095336 0.9519420
## Joint P-value (lower = worse): 0.200977 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 8
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -35.7820 59.295 0.34234 0.39346
## nodefactor.deg.main.1 -0.1979 62.302 0.35970 0.40652
## nodefactor.race..wa.B 36.9469 19.156 0.11060 0.12957
## nodefactor.race..wa.H 37.0260 25.121 0.14503 0.17140
## nodefactor.region.EW 0.8276 30.354 0.17525 0.25524
## nodefactor.region.OW 0.5847 60.227 0.34772 0.41981
## concurrent 0.5583 53.118 0.30668 0.35894
## nodematch.race..wa.B -8.4768 0.000 0.00000 0.00000
## nodematch.race..wa.H -36.3779 3.902 0.02253 0.02448
## nodematch.race..wa.O 36.1499 45.274 0.26139 0.29955
## nodematch.region 0.5006 51.171 0.29544 0.34839
## absdiff.sqrt.age 0.3384 58.776 0.33935 0.36948
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -152.0000 -76.000 -3.600e+01 4.000 82.000
## nodefactor.deg.main.1 -122.0000 -43.000 -1.000e+00 41.000 123.000
## nodefactor.race..wa.B -0.1648 23.835 3.684e+01 49.835 74.835
## nodefactor.race..wa.H -11.9080 20.092 3.709e+01 54.092 87.092
## nodefactor.region.EW -57.6796 -19.680 3.204e-01 21.320 61.320
## nodefactor.region.OW -117.5480 -39.548 4.520e-01 41.452 119.452
## concurrent -102.0000 -36.000 -2.274e-13 36.000 106.000
## nodematch.race..wa.B -8.4768 -8.477 -8.477e+00 -8.477 -8.477
## nodematch.race..wa.H -43.1997 -39.200 -3.620e+01 -34.200 -28.200
## nodematch.race..wa.O -51.8442 5.156 3.616e+01 66.156 125.181
## nodematch.region -99.4000 -34.400 6.000e-01 34.600 102.600
## absdiff.sqrt.age -112.6073 -39.800 3.449e-02 39.885 117.680
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges nodefactor.deg.main.1
## edges 1.0000000 0.8198562
## nodefactor.deg.main.1 0.8198562 1.0000000
## nodefactor.race..wa.B 0.4313005 0.3255904
## nodefactor.race..wa.H 0.5072312 0.4342230
## nodefactor.region.EW 0.3748150 0.3037192
## nodefactor.region.OW 0.6174627 0.4524967
## concurrent 0.9571991 0.7821126
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.1108398 0.1008701
## nodematch.race..wa.O 0.8553047 0.7037508
## nodematch.region 0.9382544 0.7732798
## absdiff.sqrt.age 0.8514302 0.7009786
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.431300539 0.50723120
## nodefactor.deg.main.1 0.325590396 0.43422300
## nodefactor.race..wa.B 1.000000000 0.04166726
## nodefactor.race..wa.H 0.041667261 1.00000000
## nodefactor.region.EW 0.086053130 0.32861488
## nodefactor.region.OW 0.240104208 0.29709720
## concurrent 0.415538244 0.48644419
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.002631761 0.40541424
## nodematch.race..wa.O 0.118865292 0.12676572
## nodematch.region 0.414601042 0.45997878
## absdiff.sqrt.age 0.370419675 0.42904036
## nodefactor.region.EW nodefactor.region.OW concurrent
## edges 0.37481503 0.61746268 0.9571991
## nodefactor.deg.main.1 0.30371915 0.45249669 0.7821126
## nodefactor.race..wa.B 0.08605313 0.24010421 0.4155382
## nodefactor.race..wa.H 0.32861488 0.29709720 0.4864442
## nodefactor.region.EW 1.00000000 0.10533570 0.3552855
## nodefactor.region.OW 0.10533570 1.00000000 0.5838964
## concurrent 0.35528551 0.58389644 1.0000000
## nodematch.race..wa.B NA NA NA
## nodematch.race..wa.H 0.12067322 0.05969139 0.1050691
## nodematch.race..wa.O 0.28254395 0.54738617 0.8169549
## nodematch.region 0.25642670 0.55057754 0.8997185
## absdiff.sqrt.age 0.31516876 0.52609908 0.8130123
## nodematch.race..wa.B nodematch.race..wa.H
## edges NA 0.110839823
## nodefactor.deg.main.1 NA 0.100870055
## nodefactor.race..wa.B NA 0.002631761
## nodefactor.race..wa.H NA 0.405414238
## nodefactor.region.EW NA 0.120673221
## nodefactor.region.OW NA 0.059691394
## concurrent NA 0.105069131
## nodematch.race..wa.B 1 NA
## nodematch.race..wa.H NA 1.000000000
## nodematch.race..wa.O NA 0.005288206
## nodematch.region NA 0.097667912
## absdiff.sqrt.age NA 0.089605887
## nodematch.race..wa.O nodematch.region
## edges 0.855304725 0.93825439
## nodefactor.deg.main.1 0.703750753 0.77327975
## nodefactor.race..wa.B 0.118865292 0.41460104
## nodefactor.race..wa.H 0.126765725 0.45997878
## nodefactor.region.EW 0.282543954 0.25642670
## nodefactor.region.OW 0.547386172 0.55057754
## concurrent 0.816954865 0.89971846
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.005288206 0.09766791
## nodematch.race..wa.O 1.000000000 0.80658655
## nodematch.region 0.806586547 1.00000000
## absdiff.sqrt.age 0.728039717 0.79747877
## absdiff.sqrt.age
## edges 0.85143015
## nodefactor.deg.main.1 0.70097862
## nodefactor.race..wa.B 0.37041968
## nodefactor.race..wa.H 0.42904036
## nodefactor.region.EW 0.31516876
## nodefactor.region.OW 0.52609908
## concurrent 0.81301231
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.08960589
## nodematch.race..wa.O 0.72803972
## nodematch.region 0.79747877
## absdiff.sqrt.age 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.13971319 0.128702939 0.173156261
## Lag 2e+05 0.04388442 0.017428892 0.026353539
## Lag 3e+05 0.02793700 0.023611888 -0.010118768
## Lag 4e+05 0.01163875 0.009140314 0.007706176
## Lag 5e+05 -0.03605219 -0.016748793 0.036056880
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.145440311 0.28833616 0.141208526
## Lag 2e+05 0.035544991 0.12817619 0.020048547
## Lag 3e+05 0.024434841 0.07481302 -0.009825489
## Lag 4e+05 0.036250854 0.03621096 0.001683992
## Lag 5e+05 -0.003411868 -0.02886311 0.016044216
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 NaN 1.000000000
## Lag 1e+05 0.14899222 NaN 0.058539472
## Lag 2e+05 0.04456520 NaN 0.017563689
## Lag 3e+05 0.04261077 NaN 0.007434991
## Lag 4e+05 0.01129189 NaN 0.012493246
## Lag 5e+05 -0.03392751 NaN -0.011574612
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.10849781 0.179369327 0.07051678
## Lag 2e+05 0.02941705 0.051163363 0.02766081
## Lag 3e+05 0.03041099 0.023195251 0.01841126
## Lag 4e+05 0.01295223 -0.004545038 0.01451789
## Lag 5e+05 -0.02834869 -0.032306862 -0.03173324
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.139257733 0.1236898540 0.17703540
## Lag 2e+05 0.039885287 0.0222384890 0.04038188
## Lag 3e+05 0.007649351 -0.0003423886 0.01880108
## Lag 4e+05 0.020154399 0.0205154954 0.01354788
## Lag 5e+05 -0.014605755 -0.0118724504 0.02344193
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.135081059 0.31651585 0.18993733
## Lag 2e+05 0.049026835 0.17148222 0.03081549
## Lag 3e+05 0.009777504 0.07647565 0.04574935
## Lag 4e+05 0.008157433 0.04631051 0.01882128
## Lag 5e+05 0.007205317 0.02059672 0.02226556
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 0.159454998 NaN 0.081580325
## Lag 2e+05 0.035118539 NaN 0.018549964
## Lag 3e+05 0.003395846 NaN 0.004348450
## Lag 4e+05 0.021246327 NaN 0.011242735
## Lag 5e+05 -0.001371869 NaN -0.002875158
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.103927100 0.163083386 0.083842318
## Lag 2e+05 0.030810679 0.033412178 0.001700140
## Lag 3e+05 -0.011384791 0.012962981 -0.006222393
## Lag 4e+05 0.021420422 0.021858736 0.013806289
## Lag 5e+05 -0.001584552 -0.009737552 -0.002305830
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.137242333 0.101411896 0.150913838
## Lag 2e+05 0.034127482 0.021568915 0.023130534
## Lag 3e+05 -0.004748598 0.008249099 0.003315418
## Lag 4e+05 0.001972682 0.011383057 0.028570061
## Lag 5e+05 -0.002808179 0.025915161 0.015094673
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.133977644 0.28919325 0.1900624184
## Lag 2e+05 0.043647415 0.11264335 0.0580636929
## Lag 3e+05 0.018218862 0.03971111 0.0052205931
## Lag 4e+05 0.006332031 0.02192362 0.0008449884
## Lag 5e+05 0.003402631 0.03567313 -0.0191547430
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 NaN 1.000000000
## Lag 1e+05 0.1421752122 NaN 0.052919078
## Lag 2e+05 0.0397052317 NaN 0.019553023
## Lag 3e+05 0.0015623460 NaN 0.014993593
## Lag 4e+05 -0.0004566350 NaN -0.003469483
## Lag 5e+05 -0.0004799436 NaN 0.003324468
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.122637676 0.154499760 0.087826922
## Lag 2e+05 0.019444208 0.059356768 0.015698532
## Lag 3e+05 -0.024867966 -0.001640872 -0.012435788
## Lag 4e+05 -0.008797848 0.012504498 0.001929872
## Lag 5e+05 -0.013465427 0.002004555 -0.002365604
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.132411198 0.118404865 0.1466966914
## Lag 2e+05 0.041240636 0.033827086 0.0220972789
## Lag 3e+05 0.027181249 -0.002651682 0.0197065228
## Lag 4e+05 0.003159960 0.007963159 0.0048696860
## Lag 5e+05 -0.004083949 -0.004528368 -0.0003965168
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.136478748 0.295991729 0.168672279
## Lag 2e+05 0.046244229 0.156852345 0.053627847
## Lag 3e+05 0.008808341 0.093329794 0.036151661
## Lag 4e+05 -0.002983562 0.044928675 0.013543004
## Lag 5e+05 -0.025175633 0.007077879 -0.004476381
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 NaN 1.00000000
## Lag 1e+05 0.1405826312 NaN 0.07332129
## Lag 2e+05 0.0548814320 NaN 0.01561381
## Lag 3e+05 0.0278564204 NaN 0.01305728
## Lag 4e+05 0.0026985105 NaN -0.01125248
## Lag 5e+05 0.0008531475 NaN 0.00464648
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.108127072 0.154246430 0.047006389
## Lag 2e+05 0.016757293 0.037275748 0.022962028
## Lag 3e+05 0.002643946 0.034484208 0.021749032
## Lag 4e+05 0.011558008 -0.004668933 -0.001762831
## Lag 5e+05 0.012306685 -0.003486687 -0.011952077
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.141632717 0.13988375 0.151326165
## Lag 2e+05 0.044780577 0.04856182 0.031116774
## Lag 3e+05 0.002162591 0.01085628 0.002043548
## Lag 4e+05 -0.031498903 -0.02181973 -0.009609612
## Lag 5e+05 -0.015614432 -0.01026008 0.001839964
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000 1.000000000
## Lag 1e+05 0.165368313 0.2934309 0.176168153
## Lag 2e+05 0.040886259 0.1330356 0.050174488
## Lag 3e+05 0.038925506 0.0699241 0.001356149
## Lag 4e+05 0.007221700 0.0392311 0.001833477
## Lag 5e+05 0.007605722 0.0333197 -0.003294361
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 NaN 1.00000000
## Lag 1e+05 0.1400503276 NaN 0.13640114
## Lag 2e+05 0.0415892957 NaN 0.01569790
## Lag 3e+05 -0.0006796036 NaN 0.01853395
## Lag 4e+05 -0.0237960053 NaN -0.01815252
## Lag 5e+05 -0.0070499191 NaN 0.00053267
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.123450227 0.1585001120 0.085275553
## Lag 2e+05 0.044752796 0.0453479661 0.049727374
## Lag 3e+05 -0.006612228 0.0002072572 0.007652525
## Lag 4e+05 -0.006875040 -0.0364444511 -0.031127014
## Lag 5e+05 -0.024700729 -0.0199476534 -0.021621214
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.126652709 0.1006060545 0.141426324
## Lag 2e+05 0.038973360 0.0193448022 0.033730916
## Lag 3e+05 0.018053761 0.0168158799 0.002246293
## Lag 4e+05 -0.004183880 0.0002421591 -0.013890067
## Lag 5e+05 0.002674925 -0.0150210666 -0.001336727
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.157705846 0.28880558 0.1634654174
## Lag 2e+05 0.056342907 0.11511483 0.0352999113
## Lag 3e+05 0.015501619 0.02893429 0.0199178616
## Lag 4e+05 0.001663469 0.02022821 -0.0009923958
## Lag 5e+05 -0.011673451 0.02558545 -0.0071625349
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.00000000
## Lag 1e+05 0.133595614 NaN 0.10040223
## Lag 2e+05 0.048797526 NaN 0.03310809
## Lag 3e+05 0.021810886 NaN 0.01646567
## Lag 4e+05 -0.004953879 NaN 0.01673215
## Lag 5e+05 0.005057670 NaN 0.01337412
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.108791112 0.1465602605 0.083488144
## Lag 2e+05 0.021704060 0.0289032449 0.007060473
## Lag 3e+05 0.010127895 0.0307439544 0.009086866
## Lag 4e+05 0.002295527 0.0025189541 0.001064291
## Lag 5e+05 -0.009318144 0.0009325025 0.009446983
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.1264582298 0.132872793 0.162770710
## Lag 2e+05 0.0372252399 0.027444154 0.034538376
## Lag 3e+05 0.0154910213 0.006424169 0.006592691
## Lag 4e+05 0.0008694627 0.021304173 -0.004365250
## Lag 5e+05 0.0015253506 0.016235147 -0.009295880
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.11602679 0.30520589 0.160730916
## Lag 2e+05 0.05014190 0.15592693 0.058814911
## Lag 3e+05 0.01188580 0.10153333 0.034323294
## Lag 4e+05 0.01061362 0.05213105 0.004769035
## Lag 5e+05 0.01520907 0.04346484 0.001555296
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 0.134512045 NaN 0.084763828
## Lag 2e+05 0.052206934 NaN 0.021466603
## Lag 3e+05 0.005622392 NaN 0.011887398
## Lag 4e+05 0.002353528 NaN -0.007245768
## Lag 5e+05 0.000712005 NaN 0.012951911
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.099314741 0.163502064 0.075288236
## Lag 2e+05 0.024496040 0.046804710 0.023592385
## Lag 3e+05 0.030811454 0.013832674 -0.005685714
## Lag 4e+05 0.004813101 -0.007391885 0.004863145
## Lag 5e+05 -0.010770395 0.002017110 -0.001830256
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.124596408 0.118507743 0.150316393
## Lag 2e+05 0.017544792 0.033917486 0.026181270
## Lag 3e+05 0.002460649 -0.005494438 -0.006594472
## Lag 4e+05 0.002107418 0.027831903 -0.007480336
## Lag 5e+05 -0.006216059 0.013157232 -0.020377980
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.131680960 0.28229332 0.17385062
## Lag 2e+05 0.027421050 0.11593237 0.03422530
## Lag 3e+05 -0.006478922 0.06810939 0.01320214
## Lag 4e+05 0.015781997 0.05505186 -0.02643374
## Lag 5e+05 0.019195731 0.04873151 -0.01407026
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.00000000
## Lag 1e+05 0.128069589 NaN 0.06632974
## Lag 2e+05 0.012360827 NaN 0.02961276
## Lag 3e+05 -0.003899899 NaN 0.01370616
## Lag 4e+05 -0.002218306 NaN 0.03467683
## Lag 5e+05 0.006621515 NaN 0.02249790
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.108513845 0.137826049 0.070734338
## Lag 2e+05 0.031524743 0.028119900 0.007289788
## Lag 3e+05 0.017275442 0.002497145 0.020008536
## Lag 4e+05 -0.002826534 0.004747343 -0.006803347
## Lag 5e+05 -0.006204573 -0.004911061 0.001279983
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.50199 -0.33035 0.05455
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.11540 -1.29528 -1.00443
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -1.38681 NaN -0.38175
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -2.14429 -1.48630 -1.51695
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.13309930 0.74113478 0.95649587
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.90812452 0.19522341 0.31517117
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.16549919 NaN 0.70264816
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.03200964 0.13720074 0.12927893
## Joint P-value (lower = worse): 0.7325997 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.373597 -0.002778 0.153684
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.910452 -0.078500 -0.316979
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.392432 NaN -0.905711
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.147853 -0.475863 -0.295566
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7087043 0.9977836 0.8778586
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3625844 0.9374307 0.7512598
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.6947388 NaN 0.3650889
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.8824587 0.6341717 0.7675614
## Joint P-value (lower = worse): 0.9993302 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.26213 -0.41795 1.92096
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.42985 -0.03358 0.18094
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.42156 NaN 0.16835
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.83883 0.45495 0.23003
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.79321890 0.67598452 0.05473736
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.66730787 0.97321105 0.85641783
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.67334888 NaN 0.86630645
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.40156267 0.64914415 0.81806899
## Joint P-value (lower = worse): 0.8245553 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3894 -0.3684 0.5667
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.2165 -0.6071 0.8934
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.3927 NaN 0.0000
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.6312 -0.1372 -0.4512
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6970068 0.7125753 0.5708852
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8285873 0.5437846 0.3716294
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.6945343 NaN 1.0000000
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.5278935 0.8909076 0.6518198
## Joint P-value (lower = worse): 0.9857331 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.2975 0.2447 0.8309
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.0881 -0.2660 -0.3648
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.3161 NaN 2.3944
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -1.2112 -0.5702 0.1637
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.76605487 0.80665071 0.40603527
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.27657206 0.79020291 0.71524540
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.75194186 NaN 0.01664686
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.22581616 0.56852261 0.86994743
## Joint P-value (lower = worse): 0.5653305 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.4502 0.4341 -0.1836
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.2607 -1.5523 -0.1754
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.0174 NaN -0.1753
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.8269 0.1675 0.5806
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6525496 0.6642188 0.8543387
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.7943405 0.1205855 0.8607860
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.9861204 NaN 0.8608347
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.4082851 0.8669399 0.5615391
## Joint P-value (lower = worse): 0.509544 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.04301 0.37883 0.09940
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.18548 0.85814 -0.29555
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.33064 NaN -0.59660
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.12550 -0.17051 0.71257
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.9656904 0.7048111 0.9208173
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8528523 0.3908162 0.7675702
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.7409175 NaN 0.5507773
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.9001254 0.8646119 0.4761119
## Joint P-value (lower = worse): 0.8187205 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.44283 -0.44618 -0.90896
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.49621 -0.92802 -0.08809
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.07957 NaN 0.77312
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -1.13582 -0.07573 -0.19772
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6578883 0.6554702 0.3633725
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.1345989 0.3533960 0.9298082
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.9365787 NaN 0.4394507
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.2560311 0.9396312 0.8432625
## Joint P-value (lower = worse): 0.6203305 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
Model 1
summary(est.p.buildup.unbal[[1]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + degrange(from = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x56421a88a210>
##
## Iterations: 81 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.9199 0.0249 0 <1e-04 ***
## deg3+ -Inf 0.0000 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.0000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.0000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 2
summary(est.p.buildup.unbal[[2]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x56423e8a3d08>
##
## Iterations: 85 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.89378 0.02871 0 <1e-04 ***
## nodefactor.race..wa.B 0.01789 0.06920 0 0.7961
## nodefactor.race..wa.H -0.13659 0.05609 0 0.0149 *
## deg3+ -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 3
summary(est.p.buildup.unbal[[3]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x56425c9e9550>
##
## Iterations: 115 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 607.5 NA NA NA
## nodefactor.race..wa.B -617.1 NA NA NA
## nodefactor.race..wa.H -617.4 NA NA NA
## nodematch.race..wa.B 601.4 NA NA NA
## nodematch.race..wa.H 616.8 NA NA NA
## nodematch.race..wa.O -617.6 NA NA NA
## deg3+ -Inf 0.0 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.0 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.0 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 4
summary(est.p.buildup.unbal[[4]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodematch("race..wa", diff = TRUE) + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x56427ad87818>
##
## Iterations: 122 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 615.74466 NA NA NA
## nodefactor.deg.main.1 -0.09889 0.03399 0 0.00362 **
## nodefactor.race..wa.B -625.22211 NA NA NA
## nodefactor.race..wa.H -625.48897 NA NA NA
## nodematch.race..wa.B 546.72404 NA NA NA
## nodematch.race..wa.H 624.93912 NA NA NA
## nodematch.race..wa.O -625.66862 NA NA NA
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 5
summary(est.p.buildup.unbal[[5]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x56429923a080>
##
## Iterations: 111 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 33.31402 NA NA NA
## nodefactor.deg.main.1 -0.11335 0.03413 0 0.000896 ***
## nodefactor.race..wa.B -42.65208 NA NA NA
## nodefactor.race..wa.H -42.88768 NA NA NA
## nodefactor.region.EW -0.16414 0.05959 0 0.005882 **
## nodefactor.region.OW -0.18322 0.03773 0 < 1e-04 ***
## nodematch.race..wa.B 18.68528 NA NA NA
## nodematch.race..wa.H 42.34976 NA NA NA
## nodematch.race..wa.O -43.07694 NA NA NA
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 6
summary(est.p.buildup.unbal[[6]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x5642b7805ef0>
##
## Iterations: 98 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 272.08378 NA NA NA
## nodefactor.deg.main.1 -0.11448 0.03404 0 0.00077 ***
## nodefactor.race..wa.B -280.88536 NA NA NA
## nodefactor.race..wa.H -281.12788 NA NA NA
## nodefactor.region.EW -0.17018 0.05950 0 0.00424 **
## nodefactor.region.OW -0.18333 0.03767 0 < 1e-04 ***
## nodematch.race..wa.B 254.02303 NA NA NA
## nodematch.race..wa.H 280.58884 NA NA NA
## nodematch.race..wa.O -281.31877 NA NA NA
## absdiff.sqrt.age -0.53450 0.03257 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 7
summary(est.p.buildup.unbal[[7]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + concurrent +
## nodematch("race..wa", diff = TRUE) + absdiff("sqrt.age") +
## degrange(from = 3) + offset(nodematch("role.class", diff = TRUE,
## keep = 1:2))
## <environment: 0x5642d5e5d5c8>
##
## Iterations: 113 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 50.52480 NA NA NA
## nodefactor.deg.main.1 -0.07950 0.02859 0 0.00543 **
## nodefactor.race..wa.B -61.38140 NA NA NA
## nodefactor.race..wa.H -61.57400 NA NA NA
## nodefactor.region.EW -0.12069 0.04938 0 0.01452 *
## nodefactor.region.OW -0.12810 0.03142 0 < 1e-04 ***
## concurrent 2.64475 0.06519 0 < 1e-04 ***
## nodematch.race..wa.B 48.53223 NA NA NA
## nodematch.race..wa.H 61.03331 NA NA NA
## nodematch.race..wa.O -61.75847 NA NA NA
## absdiff.sqrt.age -0.50948 0.03208 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 8
summary(est.p.buildup.unbal[[8]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + concurrent +
## nodematch("race..wa", diff = TRUE) + nodematch("region",
## diff = FALSE) + absdiff("sqrt.age") + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x5642f4536290>
##
## Iterations: 82 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 178.03815 NA NA NA
## nodefactor.deg.main.1 -0.07969 0.02832 0 0.00489 **
## nodefactor.race..wa.B -190.44423 NA NA NA
## nodefactor.race..wa.H -190.63071 NA NA NA
## nodefactor.region.EW 0.59640 0.03873 0 < 1e-04 ***
## nodefactor.region.OW 0.16682 0.02195 0 < 1e-04 ***
## concurrent 2.64387 0.06512 0 < 1e-04 ***
## nodematch.race..wa.B 173.35754 NA NA NA
## nodematch.race..wa.H 190.02620 NA NA NA
## nodematch.race..wa.O -190.82038 NA NA NA
## nodematch.region 1.91123 0.06001 0 < 1e-04 ***
## absdiff.sqrt.age -0.50961 0.03245 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Network diagnostics
Model 1
(dx_pers1 <- netdx(est.p.buildup.unbal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2018 2057.895 0.02 42.088
## nodefactor.deg.main.1 NA 1845.488 NA 47.615
## nodefactor.race..wa.B NA 247.272 NA 15.442
## nodefactor.race..wa.H NA 443.371 NA 21.282
## nodefactor.region.EW NA 415.548 NA 19.340
## nodefactor.region.OW NA 1346.859 NA 40.156
## concurrent NA 626.428 NA 28.475
## nodematch.race..wa.B NA 7.352 NA 2.686
## nodematch.race..wa.H NA 23.814 NA 4.766
## nodematch.race..wa.O NA 1424.703 NA 35.908
## nodematch.region NA 908.639 NA 32.078
## absdiff.sqrt.age NA 2345.125 NA 55.975
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.571 -0.032 30.073
## Pct Edges Diss 0.032 0.032 0.000 0.004plot(dx_pers1, type="formation")
plot(dx_pers1, type="duration")
plot(dx_pers1, type="dissolution")
Model 2
(dx_pers2 <- netdx(est.p.buildup.unbal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2018.000 2054.666 0.018 41.525
## nodefactor.deg.main.1 NA 1839.277 NA 48.510
## nodefactor.race..wa.B 251.165 257.159 0.024 15.822
## nodefactor.race..wa.H 388.908 395.721 0.018 20.233
## nodefactor.region.EW NA 412.530 NA 20.538
## nodefactor.region.OW NA 1351.482 NA 40.068
## concurrent NA 626.768 NA 27.840
## nodematch.race..wa.B NA 8.222 NA 2.871
## nodematch.race..wa.H NA 19.189 NA 4.279
## nodematch.race..wa.O NA 1453.966 NA 35.756
## nodematch.region NA 913.415 NA 30.303
## absdiff.sqrt.age NA 2343.328 NA 56.223
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.544 -0.033 30.016
## Pct Edges Diss 0.032 0.032 0.002 0.004plot(dx_pers2, type="formation")
plot(dx_pers2, type="duration")
plot(dx_pers2, type="dissolution")
Model 3
(dx_pers3 <- netdx(est.p.buildup.unbal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2018.000 2018.726 0.000 39.456
## nodefactor.deg.main.1 NA 1800.528 NA 47.466
## nodefactor.race..wa.B 251.165 293.109 0.167 15.135
## nodefactor.race..wa.H 388.908 434.039 0.116 19.772
## nodefactor.region.EW NA 404.549 NA 19.558
## nodefactor.region.OW NA 1321.244 NA 40.595
## concurrent NA 606.615 NA 28.988
## nodematch.race..wa.B 8.477 0.000 -1.000 0.000
## nodematch.race..wa.H 51.200 15.300 -0.701 3.950
## nodematch.race..wa.O 1246.844 1306.879 0.048 33.908
## nodematch.region NA 898.638 NA 29.164
## absdiff.sqrt.age NA 2296.794 NA 56.807
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.538 -0.033 29.972
## Pct Edges Diss 0.032 0.032 0.000 0.004plot(dx_pers3, type="formation")
plot(dx_pers3, type="duration")
plot(dx_pers3, type="dissolution")
Model 4
(dx_pers4 <- netdx(est.p.buildup.unbal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2018.000 2021.060 0.002 42.433
## nodefactor.deg.main.1 1684.000 1722.241 0.023 48.094
## nodefactor.race..wa.B 251.165 292.842 0.166 14.754
## nodefactor.race..wa.H 388.908 432.905 0.113 20.554
## nodefactor.region.EW NA 405.103 NA 19.242
## nodefactor.region.OW NA 1328.640 NA 38.623
## concurrent NA 606.778 NA 29.112
## nodematch.race..wa.B 8.477 0.000 -1.000 0.000
## nodematch.race..wa.H 51.200 14.767 -0.712 3.977
## nodematch.race..wa.O 1246.844 1310.079 0.051 35.492
## nodematch.region NA 896.225 NA 28.455
## absdiff.sqrt.age NA 2303.437 NA 62.094
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.555 -0.032 30.047
## Pct Edges Diss 0.032 0.032 0.001 0.004plot(dx_pers4, type="formation")
plot(dx_pers4, type="duration")
plot(dx_pers4, type="dissolution")
Model 5
(dx_pers5 <- netdx(est.p.buildup.unbal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2018.000 2021.875 0.002 41.425
## nodefactor.deg.main.1 1684.000 1721.054 0.022 44.213
## nodefactor.race..wa.B 251.165 292.444 0.164 16.030
## nodefactor.race..wa.H 388.908 435.053 0.119 19.648
## nodefactor.region.EW 367.680 375.074 0.020 19.006
## nodefactor.region.OW 1182.548 1207.386 0.021 36.643
## concurrent NA 612.487 NA 28.726
## nodematch.race..wa.B 8.477 0.000 -1.000 0.000
## nodematch.race..wa.H 51.200 15.552 -0.696 3.849
## nodematch.race..wa.O 1246.844 1309.929 0.051 33.890
## nodematch.region NA 947.001 NA 29.319
## absdiff.sqrt.age NA 2307.299 NA 60.492
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.588 -0.031 30.016
## Pct Edges Diss 0.032 0.032 0.000 0.004plot(dx_pers5, type="formation")
plot(dx_pers5, type="duration")
plot(dx_pers5, type="dissolution")
Model 6
(dx_pers6 <- netdx(est.p.buildup.unbal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2018.000 2021.521 0.002 43.189
## nodefactor.deg.main.1 1684.000 1716.523 0.019 45.235
## nodefactor.race..wa.B 251.165 293.533 0.169 16.744
## nodefactor.race..wa.H 388.908 432.091 0.111 19.612
## nodefactor.region.EW 367.680 375.661 0.022 20.040
## nodefactor.region.OW 1182.548 1208.962 0.022 37.301
## concurrent NA 613.623 NA 28.184
## nodematch.race..wa.B 8.477 0.000 -1.000 0.000
## nodematch.race..wa.H 51.200 15.251 -0.702 3.680
## nodematch.race..wa.O 1246.844 1311.148 0.052 33.684
## nodematch.region NA 943.743 NA 30.265
## absdiff.sqrt.age 1665.254 1700.086 0.021 48.025
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.594 -0.031 30.099
## Pct Edges Diss 0.032 0.032 -0.001 0.004plot(dx_pers6, type="formation")
plot(dx_pers6, type="duration")
plot(dx_pers6, type="dissolution")
Model 7
(dx_pers7 <- netdx(est.p.buildup.unbal[[7]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2018.000 2124.177 0.053 67.419
## nodefactor.deg.main.1 1684.000 1804.936 0.072 66.864
## nodefactor.race..wa.B 251.165 307.149 0.223 19.593
## nodefactor.race..wa.H 388.908 455.242 0.171 25.994
## nodefactor.region.EW 367.680 394.691 0.073 25.683
## nodefactor.region.OW 1182.548 1272.340 0.076 49.874
## concurrent 1385.000 1473.359 0.064 59.084
## nodematch.race..wa.B 8.477 0.000 -1.000 0.000
## nodematch.race..wa.H 51.200 16.309 -0.681 4.242
## nodematch.race..wa.O 1246.844 1378.095 0.105 49.507
## nodematch.region NA 994.251 NA 37.912
## absdiff.sqrt.age 1665.254 1784.561 0.072 66.569
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.530 -0.033 29.926
## Pct Edges Diss 0.032 0.032 0.002 0.004plot(dx_pers7, type="formation")
plot(dx_pers7, type="duration")
plot(dx_pers7, type="dissolution")
Model 8
(dx_pers8 <- netdx(est.p.buildup.unbal[[8]], nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2018.000 2123.648 0.052 73.796
## nodefactor.deg.main.1 1684.000 1812.342 0.076 69.314
## nodefactor.race..wa.B 251.165 306.902 0.222 19.369
## nodefactor.race..wa.H 388.908 455.392 0.171 27.489
## nodefactor.region.EW 367.680 395.316 0.075 31.176
## nodefactor.region.OW 1182.548 1275.678 0.079 66.117
## concurrent 1385.000 1472.639 0.063 62.794
## nodematch.race..wa.B 8.477 0.000 -1.000 0.000
## nodematch.race..wa.H 51.200 15.760 -0.692 4.078
## nodematch.race..wa.O 1246.844 1377.114 0.104 55.860
## nodematch.region 1614.400 1728.863 0.071 61.538
## absdiff.sqrt.age 1665.254 1787.982 0.074 69.944
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.535 -0.033 29.982
## Pct Edges Diss 0.032 0.032 0.000 0.004plot(dx_pers8, type="formation")
plot(dx_pers8, type="duration")
plot(dx_pers8, type="dissolution")